Activity Monitoring Device with Assessement of Exercise Intensity

ABSTRACT

Aspects relate to a portable device that may be used to identify a critical intensity and an anaerobic work capacity of an individual. The device may utilize muscle oxygen sensor data, speed data, or power data. The device may utilize data from multiple exercise sessions, or may utilize data from a single exercise session. The device may additionally estimate a critical intensity from a previous race time input from a user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/US2016/027771, filed Apr. 15, 2016, which claims priority to U.S.Provisional Application No. 62/148,027, filed Apr. 15, 2015, U.S.Provisional Patent Application No. 62/168,059, filed on May 29, 2015,U.S. Provisional Patent Application No. 62/168,066, filed May 29, 2015,U.S. Provisional Patent Application No. 62/168,079, filed May 29, 2015,U.S. Provisional Patent Application No. 62/168,095, filed May 29, 2015,and U.S. Provisional Patent Application No. 62/168,110, filed May 29,2015. This application claims priority to U.S. Provisional PatentApplication No. 62/168,059, filed on May 29, 2015, U.S. ProvisionalPatent Application No. 62/168,066, filed May 29, 2015, U.S. ProvisionalPatent Application No. 62/168,079, filed May 29, 2015, U.S. ProvisionalPatent Application No. 62/168,095, filed May 29, 2015, and U.S.Provisional Patent Application No. 62/168,110, filed May 29, 2015, whichare expressly incorporated herein by reference in their entireties forany and all non-limiting purposes.

BACKGROUND

While most people appreciate the importance of physical fitness, manyhave difficulty finding the motivation required to maintain a regularexercise program. Some people find it particularly difficult to maintainan exercise regimen that involves continuously repetitive motions, suchas running, walking and bicycling. Devices for tracking a user'sactivity may offer motivation in this regard, providing feedback on pastactivity, and encouragement to continue with an exercise routine inorder to meet various exercise goals.

However, certain exercise metrics for athletes are assessed in formallab-based settings, and using cumbersome equipment to monitor anindividual while he/she exercises at a fixed location (e.g. on atreadmill or stationary bike). As such, these exercise metrics may notbe readily available to the general population. Therefore, improvedsystems and methods to address at least one or more of theseshortcomings in the art are desired.

BRIEF SUMMARY

The following presents a simplified summary of the present disclosure inorder to provide a basic understanding of some aspects of the invention.This summary is not an extensive overview of the invention. It is notintended to identify key or critical elements of the invention or todelineate the scope of the invention. The following summary merelypresents some concepts of the invention in a simplified form as aprelude to the more detailed description provided below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system that may be configured to providepersonal training and/or obtain data from the physical movements of auser in accordance with example embodiments;

FIG. 2 illustrates an example computer device that may be part of or incommunication with the system of FIG. 1.

FIG. 3 shows an illustrative sensor assembly that may be worn by a userin accordance with example embodiments;

FIG. 4 shows another example sensor assembly that may be worn by a userin accordance with example embodiments;

FIG. 5 shows illustrative locations for sensory input which may includephysical sensors located on/in a user's clothing and/or be based uponidentification of relationships between two moving body parts of theuser;

FIGS. 6A-6C depict graphs of exercise data associated with threeexercise intensity domains, according to one or more aspects describedherein;

FIG. 7 schematically depicts an activity monitoring device, according toone or more aspects described herein;

FIG. 8 schematically depicts a flowchart diagram for calculation of acritical tissue oxygenation percentage and/or an anaerobic workcapacity, from a tissue oxygenation sensor data, according to one ormore aspects described herein;

FIGS. 9A-9B depict graphs of muscle oxygenation sensor data frommultiple exercise sessions, according to one or more aspects describedherein;

FIG. 10 depicts a flowchart diagram for determination as to whether auser is exercising at an unsustainable work rate within a severeexercise intensity domain, according to one or more aspects describedherein;

FIG. 11 depicts a flowchart diagram for determination as to whether auser is exercising at an unsustainable, a sustainable, or a criticalwork rate, according to one or more aspects described herein;

FIG. 12 depicts graphs of speed and muscle oxygenation output datagenerated during an exercise session, according to one or more aspectsdescribed herein;

FIG. 13 depicts a flowchart diagram for determination as to whether auser is exercising within a severe exercise intensity domain, accordingto one or more aspects described herein;

FIGS. 14A-14B depict graphs of power and muscle oxygenation output datafrom two exercise sessions, according to one or more aspects describedherein;

FIG. 15 depicts a flowchart diagram for determination as to whether thereceived tissue oxygenation data represents exercise at a criticalintensity, according to one or more aspects described herein;

FIG. 16 depicts a graph of muscle oxygenation percentage for differentexercise sessions, according to one or more aspects described herein;

FIG. 17 depicts graphs of speed and muscle oxygenation output datagenerated during a same exercise session, according to one or moreaspects described herein;

FIG. 18 depicts a graph of power output data from an exercise session,according to one or more aspects described herein;

FIG. 19 depicts a flowchart diagram for calculation of a critical powerassociated with an exercise session, according to one or more aspectsdescribed herein;

FIG. 20 depicts a graph of output speed data for an exercise session,according to one or more aspects described herein;

FIG. 21 is a flowchart diagram that may be utilized to calculate acritical speed and an anaerobic work capacity based upon speed sensordata, according to one or more aspects described herein;

FIG. 22 is a flowchart diagram that may be utilized to calculate acritical speed and/or an anaerobic work capacity of the user, accordingto one or more aspects described herein;

FIG. 23 is a chart that plots distance data for multiple exercisesessions of a user, according to one or more aspects described herein;

FIG. 24 schematically depicts a model for prediction of a criticalvelocity fraction for running, according to one or more aspectsdescribed herein;

FIG. 25 schematically depicts a model for prediction of a criticalvelocity fraction for running, according to one or more aspectsdescribed herein;

FIG. 26 schematically depicts a model for prediction of a criticalvelocity fraction for cycling, according to one or more aspectsdescribed herein;

FIG. 27 schematically depicts a model for prediction of a criticalvelocity fraction for cycling, according to one or more aspectsdescribed herein;

FIG. 28 is a flowchart diagram that may be utilized to calculate acritical velocity and an anaerobic work capacity based upon a singleinput data point, according to one or more aspects described herein;

FIG. 29 is a flowchart diagram that may be utilized to estimate a volumeof oxygen consumption in response to a received rate of perceivedexertion of the user, according to one or more aspects described herein;and

FIG. 30 schematically depicts an anaerobic work capacity replenishmentrate, according to one or more aspects described herein.

DETAILED DESCRIPTION

Aspects of this disclosure involve obtaining, storing, and/or processingathletic data relating to the physical movements of an athlete. Theathletic data may be actively or passively sensed and/or stored in oneor more non-transitory storage mediums. Still further aspects relate tousing athletic data to generate an output, such as for example,calculated athletic attributes, feedback signals to provide guidance,and/or other information. These and other aspects will be discussed inthe context of the following illustrative examples of a personaltraining system.

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration various embodiments in whichaspects of the disclosure may be practiced. It is to be understood thatother embodiments may be utilized and structural and functionalmodifications may be made without departing from the scope and spirit ofthe present disclosure. Further, headings within this disclosure shouldnot be considered as limiting aspects of the disclosure and the exampleembodiments are not limited to the example headings.

I. Example Personal Training System

A. Illustrative Networks

Aspects of this disclosure relate to systems and methods that may beutilized across a plurality of networks. In this regard, certainembodiments may be configured to adapt to dynamic network environments.Further embodiments may be operable in differing discrete networkenvironments. FIG. 1 illustrates an example of a personal trainingsystem 100 in accordance with example embodiments. Example system 100may include one or more interconnected networks, such as theillustrative body area network (BAN) 102, local area network (LAN) 104,and wide area network (WAN) 106. As shown in FIG. 1 (and describedthroughout this disclosure), one or more networks (e.g., BAN 102, LAN104, and/or WAN 106), may overlap or otherwise be inclusive of eachother. Those skilled in the art will appreciate that the illustrativenetworks 102-106 are logical networks that may each comprise one or moredifferent communication protocols and/or network architectures and yetmay be configured to have gateways to each other or other networks. Forexample, each of BAN 102, LAN 104 and/or WAN 106 may be operativelyconnected to the same physical network architecture, such as cellularnetwork architecture 108 and/or WAN architecture 110. For example,portable electronic device 112, which may be considered a component ofboth BAN 102 and LAN 104, may comprise a network adapter or networkinterface card (NIC) configured to translate data and control signalsinto and from network messages according to one or more communicationprotocols, such as the Transmission Control Protocol (TCP), the InternetProtocol (IP), and the User Datagram Protocol (UDP) through one or moreof architectures 108 and/or 110. These protocols are well known in theart, and thus will not be discussed here in more detail.

Network architectures 108 and 110 may include one or more informationdistribution network(s), of any type(s) or topology(s), alone or incombination(s), such as for example, cable, fiber, satellite, telephone,cellular, wireless, etc. and as such, may be variously configured suchas having one or more wired or wireless communication channels(including but not limited to: WiFi®, Bluetooth®, Near-FieldCommunication (NFC) and/or ANT technologies). Thus, any device within anetwork of FIG. 1, (such as portable electronic device 112 or any otherdevice described herein) may be considered inclusive to one or more ofthe different logical networks 102-106. With the foregoing in mind,example components of an illustrative BAN and LAN (which may be coupledto WAN 106) will be described.

1. Example Local Area Network

LAN 104 may include one or more electronic devices, such as for example,computer device 114. Computer device 114, or any other component ofsystem 100, may comprise a mobile terminal, such as a telephone, musicplayer, tablet, netbook or any portable device. In other embodiments,computer device 114 may comprise a media player or recorder, desktopcomputer, server(s), a gaming console, such as for example, a Microsoft®XBOX, Sony® Playstation, and/or a Nintendo® Wii gaming consoles. Thoseskilled in the art will appreciate that these are merely example devicesfor descriptive purposes and this disclosure is not limited to anyconsole or computing device.

Those skilled in the art will appreciate that the design and structureof computer device 114 may vary depending on several factors, such asits intended purpose. One example implementation of computer device 114is provided in FIG. 2, which illustrates a block diagram of computingdevice 200. Those skilled in the art will appreciate that the disclosureof FIG. 2 may be applicable to any device disclosed herein. Device 200may include one or more processors, such as processor 202-1 and 202-2(generally referred to herein as “processors 202” or “processor 202”).Processors 202 may communicate with each other or other components viaan interconnection network or bus 204. Processor 202 may include one ormore processing cores, such as cores 206-1 and 206-2 (referred to hereinas “cores 206” or more generally as “core 206”), which may beimplemented on a single integrated circuit (IC) chip.

Cores 206 may comprise a shared cache 208 and/or a private cache (e.g.,caches 210-1 and 210-2, respectively). One or more caches 208/210 maylocally cache data stored in a system memory, such as memory 212, forfaster access by components of the processor 202. Memory 212 may be incommunication with the processors 202 via a chipset 216. Cache 208 maybe part of system memory 212 in certain embodiments. Memory 212 mayinclude, but is not limited to, random access memory (RAM), read onlymemory (ROM), and include one or more of solid-state memory, optical ormagnetic storage, and/or any other medium that can be used to storeelectronic information. Yet other embodiments may omit system memory212.

System 200 may include one or more I/O devices (e.g., I/O devices 214-1through 214-3, each generally referred to as I/O device 214). I/O datafrom one or more I/O devices 214 may be stored at one or more caches208, 210 and/or system memory 212. Each of I/O devices 214 may bepermanently or temporarily configured to be in operative communicationwith a component of system 100 using any physical or wirelesscommunication protocol.

Returning to FIG. 1, four example I/O devices (shown as elements116-122) are shown as being in communication with computer device 114.Those skilled in the art will appreciate that one or more of devices116-122 may be stand-alone devices or may be associated with anotherdevice besides computer device 114. For example, one or more I/O devicesmay be associated with or interact with a component of BAN 102 and/orWAN 106. I/O devices 116-122 may include, but are not limited toathletic data acquisition units, such as for example, sensors. One ormore I/O devices may be configured to sense, detect, and/or measure anathletic parameter from a user, such as user 124. Examples include, butare not limited to: an accelerometer, a gyroscope, alocation-determining device (e.g., GPS), light (including non-visiblelight) sensor, temperature sensor (including ambient temperature and/orbody temperature), sleep pattern sensors, heart rate monitor,image-capturing sensor, moisture sensor, force sensor, compass, angularrate sensor, and/or combinations thereof among others.

In further embodiments, I/O devices 116-122 may be used to provide anoutput (e.g., audible, visual, or tactile cue) and/or receive an input,such as a user input from athlete 124. Example uses for theseillustrative I/O devices are provided below, however, those skilled inthe art will appreciate that such discussions are merely descriptive ofsome of the many options within the scope of this disclosure. Further,reference to any data acquisition unit, I/O device, or sensor is to beinterpreted disclosing an embodiment that may have one or more I/Odevice, data acquisition unit, and/or sensor disclosed herein or knownin the art (either individually or in combination).

Information from one or more devices (across one or more networks) maybe used to provide (or be utilized in the formation of) a variety ofdifferent parameters, metrics or physiological characteristics includingbut not limited to: motion parameters, such as speed, acceleration,distance, steps taken, direction, relative movement of certain bodyportions or objects to others, or other motion parameters which may beexpressed as angular rates, rectilinear rates or combinations thereof,physiological parameters, such as calories, heart rate, sweat detection,effort, oxygen consumed, oxygen kinetics, and other metrics which mayfall within one or more categories, such as: pressure, impact forces,information regarding the athlete, such as height, weight, age,demographic information and combinations thereof.

System 100 may be configured to transmit and/or receive athletic data,including the parameters, metrics, or physiological characteristicscollected within system 100 or otherwise provided to system 100. As oneexample, WAN 106 may comprise server 111. Server 111 may have one ormore components of system 200 of FIG. 2. In one embodiment, server 111comprises at least a processor and a memory, such as processor 206 andmemory 212. Server 111 may be configured to store computer-executableinstructions on a non-transitory computer-readable medium. Theinstructions may comprise athletic data, such as raw or processed datacollected within system 100. System 100 may be configured to transmitdata, such as energy expenditure points, to a social networking websiteor host such a site. Server 111 may be utilized to permit one or moreusers to access and/or compare athletic data. As such, server 111 may beconfigured to transmit and/or receive notifications based upon athleticdata or other information.

Returning to LAN 104, computer device 114 is shown in operativecommunication with a display device 116, an image-capturing device 118,sensor 120 and exercise device 122, which are discussed in turn belowwith reference to example embodiments. In one embodiment, display device116 may provide audio-visual cues to athlete 124 to perform a specificathletic movement. The audio-visual cues may be provided in response tocomputer-executable instruction executed on computer device 114 or anyother device, including a device of BAN 102 and/or WAN. Display device116 may be a touchscreen device or otherwise configured to receive auser-input.

In one embodiment, data may be obtained from image-capturing device 118and/or other sensors, such as sensor 120, which may be used to detect(and/or measure) athletic parameters, either alone or in combinationwith other devices, or stored information. Image-capturing device 118and/or sensor 120 may comprise a transceiver device. In one embodimentsensor 128 may comprise an infrared (IR), electromagnetic (EM) oracoustic transceiver. For example, image-capturing device 118, and/orsensor 120 may transmit waveforms into the environment, includingtowards the direction of athlete 124 and receive a “reflection” orotherwise detect alterations of those released waveforms. Those skilledin the art will readily appreciate that signals corresponding to amultitude of different data spectrums may be utilized in accordance withvarious embodiments. In this regard, devices 118 and/or 120 may detectwaveforms emitted from external sources (e.g., not system 100). Forexample, devices 118 and/or 120 may detect heat being emitted from user124 and/or the surrounding environment. Thus, image-capturing device 126and/or sensor 128 may comprise one or more thermal imaging devices. Inone embodiment, image-capturing device 126 and/or sensor 128 maycomprise an IR device configured to perform range phenomenology.

In one embodiment, exercise device 122 may be any device configurable topermit or facilitate the athlete 124 performing a physical movement,such as for example a treadmill, step machine, etc. There is norequirement that the device be stationary. In this regard, wirelesstechnologies permit portable devices to be utilized, thus a bicycle orother mobile exercising device may be utilized in accordance withcertain embodiments. Those skilled in the art will appreciate thatequipment 122 may be or comprise an interface for receiving anelectronic device containing athletic data performed remotely fromcomputer device 114. For example, a user may use a sporting device(described below in relation to BAN 102) and upon returning home or thelocation of equipment 122, download athletic data into element 122 orany other device of system 100. Any I/O device disclosed herein may beconfigured to receive activity data.

2. Body Area Network

BAN 102 may include two or more devices configured to receive, transmit,or otherwise facilitate the collection of athletic data (includingpassive devices). Exemplary devices may include one or more dataacquisition units, sensors, or devices known in the art or disclosedherein, including but not limited to I/O devices 116-122. Two or morecomponents of BAN 102 may communicate directly, yet in otherembodiments, communication may be conducted via a third device, whichmay be part of BAN 102, LAN 104, and/or WAN 106. One or more componentsof LAN 104 or WAN 106 may form part of BAN 102. In certainimplementations, whether a device, such as portable device 112, is partof BAN 102, LAN 104, and/or WAN 106, may depend on the athlete'sproximity to an access point to permit communication with mobilecellular network architecture 108 and/or WAN architecture 110. Useractivity and/or preference may also influence whether one or morecomponents are utilized as part of BAN 102. Example embodiments areprovided below.

User 124 may be associated with (e.g., possess, carry, wear, and/orinteract with) any number of devices, such as portable device 112,shoe-mounted device 126, wrist-worn device 128 and/or a sensinglocation, such as sensing location 130, which may comprise a physicaldevice or a location that is used to collect information. One or moredevices 112, 126, 128, and/or 130 may not be specially designed forfitness or athletic purposes. Indeed, aspects of this disclosure relateto utilizing data from a plurality of devices, some of which are notfitness devices, to collect, detect, and/or measure athletic data. Incertain embodiments, one or more devices of BAN 102 (or any othernetwork) may comprise a fitness or sporting device that is specificallydesigned for a particular sporting use. As used herein, the term“sporting device” includes any physical object that may be used orimplicated during a specific sport or fitness activity. Exemplarysporting devices may include, but are not limited to: golf balls,basketballs, baseballs, soccer balls, footballs, powerballs, hockeypucks, weights, bats, clubs, sticks, paddles, mats, and combinationsthereof. In further embodiments, exemplary fitness devices may includeobjects within a sporting environment where a specific sport occurs,including the environment itself, such as a goal net, hoop, backboard,portions of a field, such as a midline, outer boundary marker, base, andcombinations thereof.

In this regard, those skilled in the art will appreciate that one ormore sporting devices may also be part of (or form) a structure andvice-versa, a structure may comprise one or more sporting devices or beconfigured to interact with a sporting device. For example, a firststructure may comprise a basketball hoop and a backboard, which may beremovable and replaced with a goal post. In this regard, one or moresporting devices may comprise one or more sensors, such as one or moreof the sensors discussed above in relation to FIGS. 1-3, that mayprovide information utilized, either independently or in conjunctionwith other sensors, such as one or more sensors associated with one ormore structures. For example, a backboard may comprise a first sensorconfigured to measure a force and a direction of the force by abasketball upon the backboard and the hoop may comprise a second sensorto detect a force. Similarly, a golf club may comprise a first sensorconfigured to detect grip attributes on the shaft and a second sensorconfigured to measure impact with a golf ball.

Looking to the illustrative portable device 112, it may be amulti-purpose electronic device, that for example, includes a telephoneor digital music player, including an IPOD®, IPAD®, or iPhone®, branddevices available from Apple, Inc. of Cupertino, Calif. or Zune® orMicrosoft® Windows devices available from Microsoft of Redmond, Wash. Asknown in the art, digital media players can serve as an output device,input device, and/or storage device for a computer. Device 112 may beconfigured as an input device for receiving raw or processed datacollected from one or more devices in BAN 102, LAN 104, or WAN 106. Inone or more embodiments, portable device 112 may comprise one or morecomponents of computer device 114. For example, portable device 112 maybe include a display 116, image-capturing device 118, and/or one or moredata acquisition devices, such as any of the I/O devices 116-122discussed above, with or without additional components, so as tocomprise a mobile terminal.

a. Illustrative Apparel/Accessory Sensors

In certain embodiments, I/O devices may be formed within or otherwiseassociated with user's 124 clothing or accessories, including a watch,armband, wristband, necklace, shirt, shoe, or the like. These devicesmay be configured to monitor athletic movements of a user. It is to beunderstood that they may detect athletic movement during user's 124interactions with computer device 114 and/or operate independently ofcomputer device 114 (or any other device disclosed herein). For example,one or more devices in BAN 102 may be configured to function as anall-day activity monitor that measures activity regardless of the user'sproximity or interactions with computer device 114. It is to be furtherunderstood that the sensory system 302 shown in FIG. 3 and the deviceassembly 400 shown in FIG. 4, each of which are described in thefollowing paragraphs, are merely illustrative examples.

i. Shoe-Mounted Device

In certain embodiments, device 126 shown in FIG. 1, may comprisefootwear which may include one or more sensors, including but notlimited to those disclosed herein and/or known in the art. FIG. 3illustrates one example embodiment of a sensor system 302 providing oneor more sensor assemblies 304. Assembly 304 may comprise one or moresensors, such as for example, an accelerometer, gyroscope,location-determining components, force sensors and/or or any othersensor disclosed herein or known in the art. In the illustratedembodiment, assembly 304 incorporates a plurality of sensors, which mayinclude force-sensitive resistor (FSR) sensors 306; however, othersensor(s) may be utilized. Port 308 may be positioned within a solestructure 309 of a shoe, and is generally configured for communicationwith one or more electronic devices. Port 308 may optionally be providedto be in communication with an electronic module 310, and the solestructure 309 may optionally include a housing 311 or other structure toreceive the module 310. The sensor system 302 may also include aplurality of leads 312 connecting the FSR sensors 306 to the port 308,to enable communication with the module 310 and/or another electronicdevice through the port 308. Module 310 may be contained within a wellor cavity in a sole structure of a shoe, and the housing 311 may bepositioned within the well or cavity. In one embodiment, at least onegyroscope and at least one accelerometer are provided within a singlehousing, such as module 310 and/or housing 311. In at least a furtherembodiment, one or more sensors are provided that, when operational, areconfigured to provide directional information and angular rate data. Theport 308 and the module 310 include complementary interfaces 314, 316for connection and communication.

In certain embodiments, at least one force-sensitive resistor 306 shownin FIG. 3 may contain first and second electrodes or electrical contacts318, 320 and a force-sensitive resistive material 322 disposed betweenthe electrodes 318, 320 to electrically connect the electrodes 318, 320together. When pressure is applied to the force-sensitive material 322,the resistivity and/or conductivity of the force-sensitive material 322changes, which changes the electrical potential between the electrodes318, 320. The change in resistance can be detected by the sensor system302 to detect the force applied on the sensor 316. The force-sensitiveresistive material 322 may change its resistance under pressure in avariety of ways. For example, the force-sensitive material 322 may havean internal resistance that decreases when the material is compressed.Further embodiments may utilize “volume-based resistance”, which may beimplemented through “smart materials.” As another example, the material322 may change the resistance by changing the degree ofsurface-to-surface contact, such as between two pieces of the forcesensitive material 322 or between the force sensitive material 322 andone or both electrodes 318, 320. In some circumstances, this type offorce-sensitive resistive behavior may be described as “contact-basedresistance.”

ii. Wrist-Worn Device

As shown in FIG. 4, device 400 (which may resemble or comprise sensorydevice 128 shown in FIG. 1), may be configured to be worn by user 124,such as around a wrist, arm, ankle, neck or the like. Device 400 mayinclude an input mechanism, such as a depressible input button 402configured to be used during operation of the device 400. The inputbutton 402 may be operably connected to a controller 404 and/or anyother electronic components, such as one or more of the elementsdiscussed in relation to computer device 114 shown in FIG. 1. Controller404 may be embedded or otherwise part of housing 406. Housing 406 may beformed of one or more materials, including elastomeric components andcomprise one or more displays, such as display 408. The display may beconsidered an illuminable portion of the device 400. The display 408 mayinclude a series of individual lighting elements or light members suchas LED lights 410. The lights may be formed in an array and operablyconnected to the controller 404. Device 400 may include an indicatorsystem 412, which may also be considered a portion or component of theoverall display 408. Indicator system 412 can operate and illuminate inconjunction with the display 408 (which may have pixel member 414) orcompletely separate from the display 408. The indicator system 412 mayalso include a plurality of additional lighting elements or lightmembers, which may also take the form of LED lights in an exemplaryembodiment. In certain embodiments, indicator system may provide avisual indication of goals, such as by illuminating a portion oflighting members of indicator system 412 to represent accomplishmenttowards one or more goals. Device 400 may be configured to display dataexpressed in terms of activity points or currency earned by the userbased on the activity of the user, either through display 408 and/orindicator system 412.

A fastening mechanism 416 can be disengaged wherein the device 400 canbe positioned around a wrist or portion of the user 124 and thefastening mechanism 416 can be subsequently placed in an engagedposition. In one embodiment, fastening mechanism 416 may comprise aninterface, including but not limited to a USB port, for operativeinteraction with computer device 114 and/or devices, such as devices 120and/or 112. In certain embodiments, fastening member may comprise one ormore magnets. In one embodiment, fastening member may be devoid ofmoving parts and rely entirely on magnetic forces.

In certain embodiments, device 400 may comprise a sensor assembly (notshown in FIG. 4). The sensor assembly may comprise a plurality ofdifferent sensors, including those disclosed herein and/or known in theart. In an example embodiment, the sensor assembly may comprise orpermit operative connection to any sensor disclosed herein or known inthe art. Device 400 and or its sensor assembly may be configured toreceive data obtained from one or more external sensors.

iii. Apparel and/or Body Location Sensing

Element 130 of FIG. 1 shows an example sensory location which may beassociated with a physical apparatus, such as a sensor, data acquisitionunit, or other device. Yet in other embodiments, it may be a specificlocation of a body portion or region that is monitored, such as via animage capturing device (e.g., image capturing device 118). In certainembodiments, element 130 may comprise a sensor, such that elements 130 aand 130 b may be sensors integrated into apparel, such as athleticclothing. Such sensors may be placed at any desired location of the bodyof user 124. Sensors 130 a/b may communicate (e.g., wirelessly) with oneor more devices (including other sensors) of BAN 102, LAN 104, and/orWAN 106. In certain embodiments, passive sensing surfaces may reflectwaveforms, such as infrared light, emitted by image-capturing device 118and/or sensor 120. In one embodiment, passive sensors located on user's124 apparel may comprise generally spherical structures made of glass orother transparent or translucent surfaces which may reflect waveforms.Different classes of apparel may be utilized in which a given class ofapparel has specific sensors configured to be located proximate to aspecific portion of the user's 124 body when properly worn. For example,golf apparel may include one or more sensors positioned on the apparelin a first configuration and yet soccer apparel may include one or moresensors positioned on apparel in a second configuration.

FIG. 5 shows illustrative locations for sensory input (see, e.g.,sensory locations 130 a-130 o). In this regard, sensors may be physicalsensors located on/in a user's clothing, yet in other embodiments,sensor locations 130 a-130 o may be based upon identification ofrelationships between two moving body parts. For example, sensorlocation 130 a may be determined by identifying motions of user 124 withan image-capturing device, such as image-capturing device 118. Thus, incertain embodiments, a sensor may not physically be located at aspecific location (such as one or more of sensor locations 130 a-130 o),but is configured to sense properties of that location, such as withimage-capturing device 118 or other sensor data gathered from otherlocations. In this regard, the overall shape or portion of a user's bodymay permit identification of certain body parts. Regardless of whetheran image-capturing device is utilized and/or a physical sensor locatedon the user 124, and/or using data from other devices, (such as sensorysystem 302), device assembly 400 and/or any other device or sensordisclosed herein or known in the art is utilized, the sensors may sensea current location of a body part and/or track movement of the bodypart. In one embodiment, sensory data relating to location 130 m may beutilized in a determination of the user's center of gravity (a.k.a,center of mass). For example, relationships between location 130 a andlocation(s) 130 f/130 l with respect to one or more of location(s) 130m-130 o may be utilized to determine if a user's center of gravity hasbeen elevated along the vertical axis (such as during a jump) or if auser is attempting to “fake” a jump by bending and flexing their knees.In one embodiment, sensor location 1306 n may be located at about thesternum of user 124. Likewise, sensor location 130 o may be locatedapproximate to the naval of user 124. In certain embodiments, data fromsensor locations 130 m-130 o may be utilized (alone or in combinationwith other data) to determine the center of gravity for user 124. Infurther embodiments, relationships between multiple sensor locations,such as sensors 130 m-130 o, may be utilized in determining orientationof the user 124 and/or rotational forces, such as twisting of user's 124torso. Further, one or more locations, such as location(s), may beutilized as (or approximate) a center of moment location. For example,in one embodiment, one or more of location(s) 130 m-130 o may serve as apoint for a center of moment location of user 124. In anotherembodiment, one or more locations may serve as a center of moment ofspecific body parts or regions.

Exercise may be categorized into multiple intensity domains. In oneexample, exercise may be categorized into four intensity domains,including: moderate, heavy, severe, and extreme, which are defined basedon distinct metabolic profiles of an athlete or user. In one example, anathlete's exertion may be monitored using a power metric. FIG. 6Adepicts three graphs; 606, 608, and 610, corresponding to three exercisesessions undertaken by a user, and such that exertion is graphed aspower (y-axis 602) versus time (x-axis 604). Accordingly, graphs 606,608, and 610 may correspond to three separate exercise sessions carriedout at an approximately constant work rate. As such, graphs 606, 608,and 610 are depicted in FIG. 6A as approximately level graphs. In onespecific example, the exercise sessions associated with graph 606, 608,and 610 may correspond to a user cycling against an approximatelyconstant resistance (approximately constant speed, approximatelyconstant gradient, approximately constant wind resistance, amongothers). In one example, each of the exercise sessions associated withgraph 606, 608, and 610 may be carried out in a controlled environment,such as a lab-based environment, and such that an athlete may cycle on astationary exercise bicycle against a controlled, and approximatelyconstant resistance, and at an approximately constant speed. As such, apower associated with an exercise session may be calculated based on aresistance applied to the exercise bicycle, and a speed at which theperson being monitored (referred to as the athlete or user) is cycling.In another example, graphs 606, 608, and 610 may correspond to threemonitored running exercise sessions carried out against an approximatelyconstant resistance (at an approximately constant speed, and anapproximately constant gradient). As such, graphs 606, 608, and 610 maycorrespond to a user running on a treadmill at approximately constantspeed and an approximately constant gradient. Additionally oralternatively, graphs 606, 608, and 610 may correspond to three exercisesessions monitored as a user cycles at three approximately constant workrates (approximately constant power) in a non-lab-based environment on aregular bicycle, or as a user runs at three approximately constant workrates in a non-lab-based environment. Furthermore, graphs 606, 608, and610 may correspond to alternative forms of exercise (e.g. cross-countryskiing, speed skating, among others).

Graphs 606, 608, and 610 schematically depict a same exercise typecarried out by a same user at three approximately constant work ratescorresponding to three different exercise intensity domains for thatuser. In particular, graph 610 may correspond to a moderate exerciseintensity domain, graph 608 corresponds to a heavy exercise intensitydomain, and graph 606 corresponds to a severe exercise intensity domain.In one example, a moderate exercise intensity domain may be defined ascorresponding to an exercise intensity (power level) below a lactatethreshold (LT), which is schematically depicted as threshold line 612 inFIG. 6A, and otherwise referred to as a gas exchange threshold (GET),lactate inflection point (LIP), or anaerobic threshold (AT). As such, alactate threshold may correspond to an exercise intensity at whichlactate (in particular, lactic acid) starts to accumulate in thebloodstream of the exercising user. In one specific example, graph 610may be approximately 10% below the lactate threshold for the user beingmonitored.

In one example, graph 608 may correspond to a heavy exercise intensitydomain, and such that a heavy exercise intensity domain may be definedas an exercise intensity carried out between the lactate thresholdassociated with line 612, and a critical intensity (CI) (otherwisereferred to as a critical power (CP)). In one example, a criticalintensity for the user associated with graphs 606, 608, and 610 may bedenoted by line 614. As such, when an exercise intensity is below thecritical intensity, any elevation in blood lactate and oxygenconsumption (VO₂) may be stabilized after approximately 10 to 15minutes. The work rate at a critical intensity may be defined as thehighest sustainable work rate for a prolonged duration that does notelicit maximal oxygen uptake. In one example, graph 608 may beapproximately 15% below the critical intensity 614.

Graph 606 may correspond to a severe exercise intensity domain. A severeexercise intensity domain may correspond to an exercise intensity abovethe critical intensity schematically depicted by line 614. As such, awork rate within the severe exercise intensity domain may lead,inexorably, to maximal oxygen consumption, which may be referred to asacute fatigue. The amount of work a user is able to do above thecritical intensity may be capacity-limited, but rate-independent. Inother words, the amount of work that a given user is able to performabove a critical intensity may be fixed, regardless of the rate at whichthe work is done (i.e. the power). This amount of work that the user isable to perform may be referred to as a finite reserve capacity, and maybe denoted as W′. In one example, the finite reserve capacity may,alternatively, be referred to as an anaerobic capacity, or anaerobicwork capacity. In one example, where the finite reserve capacity isexpressed as a distance, it may be alternatively denoted by D′. In oneexample, the severe exercise session associated with graph 606 may beapproximately 15% above the critical intensity associated with line 614.

Line 616 schematically denotes maximal oxygen consumption (VO_(2max))for the user associated with graphs 606, 608, and 610. This maximaloxygen consumption may, alternatively, be referred to as maximal oxygenuptake, peak oxygen uptake, or maximal aerobic capacity, and may be themaximum rate of oxygen consumption for the user. In one example, themaximal oxygen consumption may be expressed in liters of oxygen perminute (L/min), or in milliliters of oxygen per kilogram of body massper minute (mL/(kg·min)).

The length of each of graphs 606, 608, and 610 corresponds to thedurations of the three exercise sessions within the moderate (graph610), heavy (graph 608), and severe (graph 606) exercise intensitydomains. Accordingly, the severe exercise intensity session 606 wascarried out for a duration corresponding to time 618. Similarly, theheavy exercise intensity session 608 was carried out for a durationcorresponding to time 620, and the moderate exercise intensity session610 was carried out for a duration corresponding to time 622. In oneimplementation, knowledge of the critical intensity corresponding toline 614, an intensity above the critical intensity (e.g. an intensityassociated with an exercise session corresponding to graph 606), and aduration 618 of the exercise session within the severe exerciseintensity domain (i.e. above the critical intensity) may be utilized tocalculate the finite reserve capacity for a user. In one example, thefinite reserve capacity may be calculated as an integration of a powergraph above the critical power. For the example of graph 606 (atconstant power), the area under the graph but above the critical powermay be calculated as:

Finite reserve capacity, W′ (J)=Intensity above a critical intensity(W)×time to fatigue (s).

In one example, once a critical intensity and a finite work capacityassociated with a given athlete are known (i.e. are identified and/orcalculated), real-time monitoring of an athletic performance of theathlete, by an activity monitoring device, such as one or more ofdevices 112, 114, 128, 200, and/or 400, among others, may be used toprovide feedback regarding a current exercise intensity relative to acritical intensity for the athlete. Additionally or alternatively, givena critical intensity and a finite work capacity associated with theathlete, the activity monitoring device may be utilized to predict oneor more outcomes of a current exercise session. As such, an activitymonitoring device may be utilized to, among others, predict a race timefor an athlete. Further details related to utilization of criticalintensity and finite work capacity information to provide feedback to auser are discussed later in the various disclosures that follow.

Muscle oxygenation, MO₂, may be utilized as a metric for monitoringexercise performance of an athlete. In one example, muscle oxygenationmay be monitored in order to identify, among others, a criticalintensity and/or anaerobic work capacity associated with an athlete. Anactivity monitoring device incorporating a muscle oxygenation sensor isdiscussed in further detail in relation to FIG. 7. FIG. 6B schematicallydepicts three graphs 628, 630, and 632 of muscle oxygenation percentage,MO₂ (%) (y-axis 624) versus time (x-axis 626). The three graphs 628,630, and 632 correspond to the three graphs 606, 608, and 610 from FIG.6A (i.e. the muscle oxygenation percentage data used to plot graphs 628,630, and 632 was received from the exercise testing associated withgraphs 606, 608, and 610). In one example, graphs 628, 630, and 632 mayeach depict muscle oxygenation percentage associated with a quadricepsmuscle of an athlete, and such that for each of graphs 628, 630, and632, the muscle oxygenation percentage data may be detected by a samesensor type (as described in further detail in relation to FIG. 7), anddetected from an approximately same location on an athlete's body (i.e.proximate a quadriceps muscle of the athlete).

Graph 628 schematically depicts a progression of muscle oxygenationpercentage of a quadriceps muscle of an athlete exercising within asevere exercise intensity domain (i.e. exercising at an intensitycorresponding to graph 606). As depicted in FIG. 6B, graph 628 depicts asteady decline in muscle oxygenation percentage, without exhibiting anincrease in muscle oxygenation percentage before the end of the exercise(point of fatigue) at time 618. Graph 630 schematically depicts aprogression of muscle oxygenation percentage of a quadriceps muscle ofan athlete exercising within a heavy exercise intensity domain (i.e.exercising at an intensity corresponding to graph 608). As depicted inFIG. 6B, graph 630 depicts a decline in muscle oxygenation percentageduring a first time period, until point 634. At point 634, graph 630exhibits a recovery in muscle oxygenation percentage before the exerciseis completed (before the athlete fatigues) at time 620. Graph 632schematically depicts a progression of muscle oxygenation percentage ofthe quadriceps muscle of the athlete exercising within a moderateexercise intensity domain (i.e. exercising at an intensity correspondingto graph 610). As depicted in FIG. 6B, graph 632 depicts a decline inmuscle oxygenation percentage during a first time period, until point636. At point 636, graph 632 exhibits a recovery in muscle oxygenationpercentage before the exercise is completed at time 622.

As previously discussed, the data used to plot graphs 628, 630, and 632may be received from a sensor configured to detect muscle oxygenation ofa quadriceps muscle of an athlete. Further, the exercise sessionsassociated with graphs 628, 630, and 632 may include cycling or runningsessions, among others, and such that the athlete's leg muscles areconsidered active muscles for the exercise sessions (i.e. as opposed tothe athlete's arm muscles, among others). In summary, FIG. 6Bschematically depicts that muscle oxygenation percentage of an activequadriceps muscle for a given exercise section may exhibit recovery froman initial decline when exercising within a moderate (graph 632) or aheavy (graph 630) exercise intensity domain, but that muscle oxygenationpercentage will not recover when exercising within a severe exerciseintensity domain.

FIG. 6C schematically depicts three graphs 644, 646, and 648 of muscleoxygenation percentage (y-axis 640) versus time (x-axis 642). The threegraphs 644, 646, and 648 correspond to the three graphs 606, 608, and610 from FIG. 6A (i.e. the muscle oxygenation percentage data used toplot graphs 644, 646, and 648 may be received from the exercise testingassociated with graphs 606, 608, and 610). In one example, graphs 644,646, and 648 may each depict muscle oxygenation percentage associatedwith a forearm muscle of the athlete. As such, graphs 644, 646, and 648may be associated with an inactive muscle for an exercise session thatconcentrates on the athlete's leg muscles (e.g. cycling or running,among others). As such, graphs 644, 646, and 648 may be plotted fromdata detected by a same sensor type (as described in further detail inrelation to FIG. 7), and detected from an approximately same location onthe athlete's body (i.e. proximate a forearm muscle of the athlete).

In one example, graphs 644, 646, and 648 may exhibit similar trends tographs 628, 630, and 632 from FIG. 6B. In particular, graph 644 depictsa steady decline in muscle oxygenation percentage, without exhibiting anincrease in muscle oxygenation percentage before the end of the exercise(at point 618). As such, graph 644 may be associated with a severeexercise intensity domain, and with graph 606 from FIG. 6A. Graph 646schematically depicts a progression of muscle oxygenation percentage ofa forearm muscle of an athlete exercising within a heavy exerciseintensity domain (i.e. exercising at an intensity corresponding to graph608). As depicted in FIG. 6C, graph 646 depicts a decline in muscleoxygenation percentage during a first time period, up until point 650.At point 650, graph 646 exhibits a recovery in muscle oxygenationpercentage before the exercise is completed at time 620. Graph 648schematically depicts a progression of muscle oxygenation percentage ofa forearm muscle of the athlete exercising within a moderate exerciseintensity domain (i.e. exercising at an intensity corresponding to graph610). As depicted in FIG. 6C, graph 648 depicts a decline in muscleoxygenation percentage during a first time period, up until point 652.At point 652, graph 648 exhibits a recovery in muscle oxygenationpercentage before the exercise is completed at time 622.

Accordingly, FIGS. 6B and 6C schematically depict that similar trends inmuscle oxygenation percentage may be exhibited by both active andinactive muscles when exercising within moderate, heavy, and severeexercise intensity domains. In this way, a muscle oxygenation sensor,such as that described in further detail in relation to FIG. 7, may bepositioned on an active or inactive muscle in order to detect usefulactivity data for an athlete.

FIG. 7 schematically depicts an activity monitoring device 700. In oneexample, the activity monitoring device 700 may include one or moreelements and/or functionality similar to devices 112, 114, 128, 200,and/or 400, among others. Accordingly, the activity monitoring device700 may comprise a processor 702, which may be similar to one or more ofprocessors 202-1 and 202-2. Processor 702 may comprise one or moreprocessing cores configured to execute one or more computationalinstructions in parallel. Additionally or alternatively, processor 702may utilize one or more processing cores to execute computationalinstructions in series, or a combination of series and parallelprocessing. Further, processor 702 may be embodied with anycomputational clock speed (clock speed may be related to a rate at whichcomputational instructions may be executed by the processor 702)disclosed herein or generally known in the art. In one example, theprocessor 702 may be configured to execute computer-executableinstructions stored on a non-transitory computer-readable medium, suchas memory 704. As such, memory 704 may be similar to memory 212, and mayinclude, but may not be limited to, persistent or volatile memory. Assuch, memory 704 may include one or more of random access memory (RAM),read only memory (ROM), solid-state memory, optical or magnetic storage,and/or any other medium that can be used to store electronicinformation.

In one implementation, electrical energy may be provided to one or moreof the components (i.e. components 702, 704, 706, 708, and/or 710) ofthe activity monitoring device 700 by a power supply 703. As such, thepower supply 703 may comprise one or more of a battery, a photovoltaiccell, a thermoelectric generator, or a wired electrical supply from anexternal source. Further, the power supply 703 may be configured tosupply one or more components of the activity monitoring device 700 withan electrical output having any voltage, and configured to supply anycurrent, without departing from the scope of these disclosures.

The activity monitoring device 700 may include a sensor 706. As such,sensor 706 may comprise an accelerometer, a gyroscope, alocation-determining device (e.g., GPS), temperature sensor (includingambient temperature and/or body temperature), sleep pattern sensors,heart rate monitor, image-capturing sensor, moisture sensor, forcesensor, compass, angular rate sensor, and/or combinations thereof amongothers. In one implementation, the activity monitoring device 700 mayinclude an interface 708. As such, the interface 708 may be embodiedwith hardware and/or firmware and software configured to facilitatecommunication between the activity monitoring device 700 and externaldevice or network, not depicted in FIG. 7. In one example, the interface708 may facilitate wireless and/or wired communication between theactivity monitoring device 700 and an external device or network. In oneexample, interface 708 may facilitate communication using one or more ofWi-Fi, Bluetooth, an Ethernet cable, a USB connection, or any otherconnection type disclosed herein or known in the art. As such, interface708 may facilitate communication between the activity monitoring device700 and an external device across a local area network (LAN), a widearea network (WAN), or the Internet, among others. Additionally oralternatively, interface 708 may facilitate communication between theactivity monitoring device 708 and a user interface. As such, a userinterface may include a display device, such as display device 116and/or one or more input interfaces (e.g. one or more button interfaces,touchscreen interfaces, microphone interfaces, and the like).

Additionally or alternatively, the activity monitoring device 700 mayinclude a muscle oxygenation sensor 710. In one example, the muscleoxygenation sensor 710 may be configured to emit electromagneticradiation in a near-infrared wavelength range. As such, the muscleoxygenation sensor 710 may utilize near-infrared spectroscopy (NIRS). Bypositioning the muscle oxygenation sensor 710 proximate an area of skin716 of a user, the emitted electromagnetic radiation, which isschematically depicted by arrow 722, may travel into the user's bodythrough, in one example, a layer of skin 716, fat 718, and into a muscletissue 720. In one example, oxyhemoglobin and deoxyhemoglobin may act aschromophores (absorbing differing amounts of light at differentwavelengths). Further, oxyhemoglobin and deoxyhemoglobin may exhibitcomparatively larger differences in absorption characteristics across arange of near infrared electromagnetic radiation. As such, the emitter712 may be configured to emit electromagnetic radiation in a nearinfrared spectrum having a wavelength range of approximately 600 to 900nm. In another example, the emitter 712 may be configured to emitinfrared light having a wavelength range of approximately 630 to 850 nm.In yet another example, the emitter 712 may be configured to emitinfrared light across another range, without departing from the scope ofthese disclosures. In one example, the emitter 712 may comprise one ormore light emitting diode (LED) elements. In one specific example, theemitter 712 may comprise four light emitting diodes.

Accordingly, a portion of light emitted from the emitter 712 may bebackscattered and detected by detector 714. In one example, line 724 mayrepresent a portion of back scattered light detected by detector 714. Assuch, the muscle oxygenation sensor 710 may be configured to calculatean attenuation in intensity between emitted and detected light. Thisattenuation may be related to an amount of light absorbed by, amongothers, the oxyhemoglobin and deoxyhemoglobin chromophores. As such, bydetecting an attenuation in emitted near infrared light, the muscleoxygenation sensor 710 may determine a concentration of oxyhemoglobin ordeoxyhemoglobin. In turn, based upon the determined concentration ofoxyhemoglobin or deoxyhemoglobin, the muscle oxygenation sensor 710 maycalculate a muscle oxygenation percentage associated with the muscle720.

In one implementation, the muscle oxygenation sensor 710 may calculate amuscle oxygenation percentage associated with muscle tissue 720according to the Beer-Lambert law:

log(I _(out) /I _(in))=ε·L·c

where I_(in) is an intensity of near-infrared radiation emitted from theemitter 712, I_(out) is an intensity of near-infrared radiation detectedby detector 714, ε is a molar attenuation coefficient of thechromophore, c is an amount concentration of the chromophore, and 1 is apath length that the emitted near infrared radiation travels through thebody (i.e. one or more of skin 716, fat 718, and muscle 720.

It will be appreciated that the light emitted from emitter 712 isschematically represented by line 722, and that portion of emitted lightdetected by detector 714 is schematically represented by line 724. Inpractice, a path of light emitted from emitter 712 traveling into auser's body (i.e. through one or more of skin 716, fat 718, and muscle720), and light detected by detector 714 may be complex, and comprise aplurality of different paths.

In one example, the muscle oxygenation sensor 710 of the activitymonitoring device 700 may be configured to be positioned proximate anarea of skin 716 of a user. As such, in one example, the emitter 712 andthe detector 714 may be positioned such that there exists substantiallyno separation between the emitter 712 and the skin 716, and similarly,substantially no separation between the detector 714 and the skin 716.In another example, the activity monitoring device 700 may be configuredto be positioned proximate an area of skin 716 of a user, such that agap between the emitter 712 and the skin 716, and/or the detector 714 inthe skin 716 does not include a layer of clothing. In yet anotherexample, the activity monitoring device 700 may be configured to bepositioned proximate an area of skin 716 of a user, such that one ormore layers of clothing may be positioned between the emitter 712 andthe skin 716, and/or the detector 714 and the skin 716.

In one example, the activity monitoring device 700, and in particular,the muscle oxygenation sensor 710, may be utilized to determine (in oneexample, to calculate) a critical intensity and/or an anaerobic workcapacity associated with a user. In one specific example, the muscleoxygenation sensor 710 may be utilized to determine a critical muscleoxygenation percentage at which a user reaches a critical intensity ofexercise. Accordingly, FIG. 8 schematically depicts a flowchart diagram800 that may be utilized to calculate one or more of a critical tissueoxygenation percentage and/or an anaerobic work capacity of a user fromdata outputted from a tissue oxygenation sensor, such as sensor 710. Assuch, the critical tissue oxygenation percentage may, in one example, bea critical tissue oxygenation percentage of muscle tissue, among others.

In one implementation, in order to calculate one or more of a criticaltissue oxygenation percentage and/or an anaerobic work capacity, a usermay provide an activity monitoring device, such as device 700, with testdata. In one example, test data may be generated by the sensor, such assensor 710, during an exercise period, which may otherwise be referredto as an exercise session. In one example, an exercise period maycomprise a prescribed duration during which a user is instructed to runas quickly as possible (i.e. as far as possible) within the prescribedtime limit. In certain specific examples, an exercise period mayinstruct a user to run as far as possible within, for example, a minute,two minutes, three minutes, four minutes, five minutes, six minutes,seven minutes, eight minutes, nine minutes, 10 minutes, 12 minutes, 15minutes, 20 minutes, or any other duration. Accordingly, a tissueoxygenation sensor, such as sensor 710, may be configured to output atissue oxygenation percentage data point each second during an exerciseperiod. Alternatively, the tissue oxygenation sensor, such as sensor710, may be configured to output a tissue oxygenation percentage datapoint at a different frequency, which may be 0.25 Hz, 0.5 Hz, 2 Hz, 3Hz, 4 Hz, or any other frequency. In one example, an exercise periodprescribed for a user in order to generate test data may ensure that theuser exercises at an intensity above a critical intensity for the userduring at least a portion of the prescribed duration of the exerciseperiod. Accordingly, in one example, one or more processes may beexecuted to instruct a user to begin an exercise period at block 802 offlowchart 800.

As previously discussed, a tissue oxygenation sensor, such as sensor710, may be used to detect, and to output data indicative of, a tissueoxygenation percentage. In one example, the tissue oxygenation sensor710 may output a data point indicative of a current tissue oxygenationpercentage for each second of an exercise period. Accordingly, in oneexample, the outputted tissue oxygenation data may be received forfurther processing by, in one example, processor 702, at block 804 offlowchart 800.

In one implementation, a tissue oxygenation percentage for each secondof a prescribed exercise period may be stored. As such, tissueoxygenation percentages for each second of a prescribed exercise periodmay be stored in, for example, memory 704. Upon completion of a givenexercise period, a number may be calculated corresponding to a totalnumber of the stored tissue oxygenation percentages for each second of aduration of a prescribed exercise period. This number may be referred toas a total number of tissue oxygenation data points for an exerciseperiod. This total number of tissue oxygenation data points may becalculated by, in one example, processor 702, and at block 806 offlowchart 800.

An exercise period utilized to generate data in order to determine acritical muscle oxygenation percentage and/or an anaerobic work capacityof the user may be summarized as a data point comprising two pieces ofinformation. This exercise period summary data point may comprise thetotal number of tissue oxygenation data points, as determined, in oneexample, at block 806, in addition to the total time (i.e. the duration)of the exercise period/session. In one example, the two pieces ofinformation (i.e. the total number of tissue oxygenation data points,and the total time) may be expressed as a coordinate point. In oneexample, this coordinate point P may be of the form P(x₁, y₁), where y₁may be the total number of tissue oxygenation data points (muscleoxygenation percentage (%)×time (s)), and x₁ may be the total time (s).In this way, the exercise period summary data point expressed as acoordinate point may be plotted, as schematically depicted in FIG. 9. Inone example, the exercise period summary data point may be calculated atblock 808 of flowchart 800.

In one implementation, in order to calculate one or more of a criticaltissue oxygenation percentage and/or an anaerobic work capacity of auser, two or more exercise period summary data points may be utilized.In one example, the durations of the exercise periods used to generatethe two or more exercise period summary data points may be different.Accordingly, in one example, one or more processes may be executed todetermine whether a threshold number of exercise periods have beencompleted by the user in order to calculate one or more of a criticaltissue oxygenation percentage and/or an anaerobic work capacity for theuser. As previously described, this threshold number of exercise periodsmay be at least two, at least three, at least four, or at least five,among others. In one specific example, one or more processes may beexecuted by processor 702 to determine whether the threshold number ofexercise periods have been completed at decision block 810 of flowchart800. Accordingly, if the threshold number of exercise periods has beenmet or exceeded, flowchart 800 proceeds to block 812. If, however, thethreshold number of exercise periods has not been met, flowchart 800proceeds from decision block 810 back to block 802.

In one example, a regression may be calculated using the two or moreexercise period summary data points that were calculated from two ormore prescribed exercise periods. This regression may be a linear, or acurvilinear regression. As such, any computational processes known inthe art for calculation of a linear or curvilinear regression may beutilized with this disclosure. In one implementation, at least a portionof a calculated regression may be utilized to determine one or more of acritical tissue oxygenation percentage and/or an anaerobic work capacityof the user. In one specific example, one or more processes may beexecuted to calculate a regression at block 812 of flowchart 800.

At least a portion of a regression calculated using the two or moreexercise period summary data points may be utilized to determine acritical tissue oxygenation percentage for a user. Specifically, thecritical tissue oxygenation percentage may correspond to a slope of theregression line (or a slope of a linear portion of a curvilinearregression). One or more processes may be executed to output a criticaltissue oxygenation percentage calculated as a slope of a regression linethrough the two or more exercise period summary data points at block 814of flowchart 800.

At least a portion of a regression calculated using the two or moreexercise period summary data points may be utilized to determine ananaerobic capacity of the user. Specifically, the anaerobic capacity maycorrespond to an intercept of the regression line (or an intercept of alinear portion of a curvilinear regression). In one example, theanaerobic capacity may be expressed as a total number of tissueoxygenation data points (tissue oxygenation percentage (%)×time (s))above a critical oxygenation percentage (%). In one implementation, oneor more processes may be executed to output an anaerobic capacitycalculated as an intercept of a regression line through the two or moreexercise period summary data points at block 816 of flowchart 800.

FIG. 9A is a chart that plots testing data from multiple exerciseperiods, or sessions, for a given user. In particular, FIG. 9A is achart 900 plotting total muscle oxygenation points 902 against time 904.Points 906, 908, and 910 are each exercise period summary data points,as described in relation to FIG. 8. As such, the exercise period summarydata points 906, 908, 910 may be calculated for a same user, and for, inthis example, three separate exercise sessions. Accordingly, each of theexercise period summary data points 906, 908, and 910 may represent aseparate exercise session. In particular, the exercise sessionsassociated with the exercise period summary data points 906, 908, and910 may have durations of approximately 300 seconds, 720 seconds, and900 seconds, respectively. Further, during the respective exercisesessions, muscle oxygenation percentage data detected for the user maybe integrated for each second of a total duration of a given exercisesession to give a total number of muscle oxygenation points equal toapproximately 6000, 25,000, and 30,000 for the respective exerciseperiod summary data points 906, 908, and 910.

In one example, the exercise period summary data points 906, 908, 910may each represent a separate exercise session, and such that a portionof each of these exercise sessions is carried out within a severeexercise intensity domain for a user. In one implementation, theexercise period summary data points 906, 908, 910 may each represent aseparate exercise session carried out in a continuous manner (nonstopexercise without breaks, e.g. continuous running and/or cycling).However, in another implementation, one or more of the exercise periodsummary data points 906, 908, 910 may each represent a separate exercisesession carried out in an intermittent manner (a non-continuous exercisesession with one or more periods of inactivity/low activity and one ormore periods of high activity, e.g. participation in team sports, suchas basketball, soccer, and the like).

In one implementation, a regression line 912 may be calculated using thethree exercise period summary data points 906, 908, and 910, as plottedon chart 900. In one example, this regression line 912 may be of theform:

y=m _(a) x+c _(a)

where y is a total number of muscle oxygenation points (y-axis), x is atime (s) (x-axis), m_(a) is the slope of the regression line 912, andc_(a) is the intercept of the regression line 912 on the y-axis.

For the example experimental data used to generate the exercise periodsummary data points 906, 908, and 910, the regression line 912 may havethe form: y=39.62x−5112.13, with an r² value of 0.99999. It is notedthat this regression line 912 formula is merely included as one exampleresult, and may not correspond to the example values discussed above forexercise period summary data points 906, 908, and 910.

In one example, a regression line, such as regression line 912, throughtwo or more exercise period summary data points, such as the exerciseperiod summary data points 906, 908, and 910, may be used to calculate acritical muscle oxygenation percentage and/or a total number of muscleoxygenation points above a critical muscle oxygenation percentage (whichmay be proportional to an anaerobic work capacity) for a user. In oneexample, given regression line 912 of the form: y=mx+c, the criticalmuscle oxygenation percentage may be equal to m, the slope of theregression line 912, and the total number of muscle oxygenation pointsabove the critical muscle oxygenation percentage may be equal to c (or|c|, the absolute value of c), the intercept of the regression line 912on the y-axis. In particular, given the experimental data depicted inchart 900, the critical muscle oxygenation percentage for the user maybe 39.62%, and the total number of muscle oxygenation points above thecritical muscle oxygenation percentage may be 5112.13.

In another example, the regression line 912 may be calculated through aplurality of exercise period summary data points greater than the threeexercise period summary data points 906, 908, 910 depicted in FIG. 9A.Further, any methodology known in the art for calculation of a linearregression may be utilized with these disclosures to calculate aregression line 912. Additionally, while FIG. 9A graphically depicts aregression line 912, the activity monitoring device 700 may beconfigured to calculate a critical muscle oxygenation percentage for auser and/or a total number of muscle oxygenation points above a criticalmuscle oxygenation percentage, without requiring that a regression linebe plotted. I.e. the activity monitoring device 700 may calculate one ormore of a critical muscle oxygenation percentage and/or a total numberof muscle oxygenation points above a critical muscle oxygenationpercentage from muscle oxygenation data outputted from the muscleoxygenation sensor 710 without calculating and/or plotting a regressionline through exercise period summary data points. As such, the depictionof regression line 912 may a pictorial description of methodology usedby the activity monitoring device 700; however the activity monitoringdevice 700 may utilize alternative computational processes to calculatethe same resulting critical muscle oxygenation percentage and/or totalnumber of muscle oxygenation points above the critical muscleoxygenation percentage.

FIG. 9B depicts a chart 920 plotting test data from multiple exerciseperiods for a same user. In particular, FIG. 9B depicts chart 920plotting muscle oxygenation percentage (%) 922 against inverse time(s⁻¹) 924. In one example, the data points 926, 928, and 930 may eachrepresent a separate exercise session. In one implementation, a datapoint, from the data points 926, 928, 930, may be of the form (x₂,y₂),where y₂ is an average muscle oxygenation percentage over a totalexercise session, and is calculated, in one example, as a sum of muscleoxygenation percentages for each second of an exercise period (inseconds), divided by the duration of the exercise period (in seconds).Those of ordinary skill in the art will recognize that the timeresolution (or sampling rate) utilized may be different than the onesecond resolution described herein, without departing from the scope ofthese disclosures. For example, y₂ may be calculated, in anotherexample, as a sum of muscle oxygenation percentages for each half secondinterval an exercise period divided by the total number of half secondsin the exercise period, among many other resolutions. Accordingly, x₂may be calculated as 1/(total duration of a given exercise period),giving a result as an inverse time, with units of seconds⁻¹ (s⁻¹). Inone specific implementation, data point 926 may include the sameinformation as exercise period summary data point 910. Similarly, datapoint 928 may include the same information as exercise period summarydata point 908, and data point 930 may include the same information asexercise period summary data point 906.

In one implementation, a regression line 932 may be calculated using twoor more data points, such as data points 926, 928, 930. In one example,the regression line 932 may be of the form:

y=m _(b) x+c _(b)

where y is a muscle oxygenation percentage (%) (y-axis), x is an inversetime (s⁻¹) (x-axis), m_(b) is the slope of the regression line 932, andc_(b) is the intercept of the regression line 932 on the y-axis.

In one example, for the specific data depicted in chart 920, theregression line 932 may have the form: y=−5102.35x+39.6, with an r²value of 0.99999. Accordingly, m_(b) the slope of the regression line932 (or |m_(b)| the absolute value), may equal the number of muscleoxygenation points above a critical muscle oxygenation percentage for auser. Similarly, c, the intercept may be equal to the critical muscleoxygenation percentage for the user.

FIG. 10 is a flowchart diagram 1000 that may be utilized to determinewhether a user is exercising at an unsustainable work rate within asevere exercise intensity domain. In one implementation, an activitymonitoring device, such as device 700, may receive tissue oxygenationdata indicative of a real-time tissue oxygenation percentage for theuser while exercising. Accordingly, this tissue oxygenation data may begenerated by, in one example, muscle oxygenation sensor 710. As such,one or more processes may be executed by, in one example, processor 702,to receive the tissue oxygenation data. These one or more processes toreceive the tissue oxygenation data may be executed at block 1002 offlowchart 1000.

In one implementation, received tissue oxygenation data may be comparedto a critical tissue oxygenation percentage for the user. As such, acritical oxygenation percentage for a user may be calculated by anactivity monitoring device, such as processor 702 of device 700, andstored memory, such as memory 704. Further, the critical muscleoxygenation percentage for a user may be calculated using one or moreprocesses described in relation to FIG. 8. In one example, the receivedtissue oxygenation data may be compared to the critical tissueoxygenation percentage for the user by processor 702. As such, one ormore processes executed by processor 702 to compare the received tissueoxygenation data to the critical tissue oxygenation percentage for theuser may be executed at block 1004 of flowchart 1000.

A comparison of received tissue oxygenation data to a critical tissueoxygenation percentage for a user may include a determination as towhether the received, real-time tissue oxygenation percentage is aboveor equal to the critical tissue oxygenation percentage. Thisdetermination may represented by decision block 1006 of flowchart 1000.Accordingly, if the received tissue oxygenation data represents a tissueoxygenation percentage that is equal to or above a critical tissueoxygenation percentage for the user, the activity monitoring device 700may output a signal indicating that the user is exercising at asustainable work rate. In one example, this output signal may becommunicated to a user via one or more indicator lights, a userinterface, an audible signal, or a haptic feedback signal, among others.As such, the output signal may be communicated through interface 708 ofthe activity monitoring device 700. In one example, the one or moreprocesses executed to output a signal indicating that the user isexercising at a sustainable work rate (i.e. outside of a severe exerciseintensity domain) may be executed at block 1008 of flowchart 1000. Inone implementation, if the received tissue oxygenation data represents atissue oxygenation percentage that is below a critical tissueoxygenation percentage for the user, the activity monitoring device 700may output a signal indicating that the user is exercising at anunsustainable work rate. In one example, this output signal may bedelivered in a similar manner to the output signal described in relationto block 1008. Further, the one or more processes executed to output asignal indicating that the user is exercising at an unsustainable workrate (i.e. within a severe exercise intensity domain) may be executed atblock 1010 of flowchart 1000.

FIG. 11 is a flowchart diagram 1100 that may be utilized to determine ifa user is exercising at an unsustainable, a sustainable, or a criticalwork rate. In one implementation, an activity monitoring device, such asdevice 700, may receive periodic data from a tissue oxygenation sensor,such as sensor 710, indicative of a real-time oxygenation percentage ofa tissue of a user while exercising. Accordingly, tissue oxygenationpercentage data may be received by, in one example, processor 702, fromsensor 710, and with a periodicity of one sample per second (1 Hz). Inanother implementation, the sensor 710 may output a data pointindicative of a tissue oxygenation percentage once every two seconds(0.5 Hz), once every three seconds (0.33 Hz), or once every four seconds(0.25 Hz), among others. Further, tissue oxygenation data may begenerated by, and received from, the sensor 710 at any rate, withoutdeparting from the scope of these disclosures. In one example, one ormore processes may be executed to receive, by a processor, such asprocessor 702, periodic data from a tissue oxygenation sensor indicativeof a tissue oxygenation percentage at block 1102 of flowchart 1100.

In one implementation, tissue oxygenation data received from a tissueoxygenation sensor, such as sensor 710, may be stored in memory, such asmemory 704. Accordingly, in one example, a trend in tissue oxygenationdata may be calculated based upon a comparison of a mostrecently-received tissue oxygenation percentage data point to one ormore previously-stored tissue oxygenation percentage data points. In oneexample, one or more processes may be executed by processor 702 of theactivity monitoring device 700, to calculate a change in tissueoxygenation percentage over a time spanning between a saved tissueoxygenation percentage data point, and a most recently-received tissueoxygenation percentage data point. In one implementation, this changemay be calculated as a positive number, which may be indicative of anincrease in tissue oxygenation percentage, as the negative number, whichmay be indicative of a decrease in tissue oxygenation percentage, or asa zero value, which may be indicative of no change in tissue oxygenationpercentage. In another implementation, one or more processes may beexecuted by processor 702 of the activity monitoring device 700 tocalculate a trend in tissue oxygenation percentage as a slope of aregression line, and calculated using two or more tissue oxygenationpercentage data points. As such, if the slope of the calculated line hasa negative value, it may be indicative of a decrease in tissueoxygenation percentage. Similarly, if the slope of the line iscalculated as having a positive value, it may be indicative of anincrease in tissue oxygenation percentage, and if the slope of the lineis calculated as having a zero value, it may be indicative of no changein tissue oxygenation. In one example, one or more processes forcalculation of a tissue oxygenation trend may be executed at block 1104of flowchart 1100.

In additional or alternative implementations, a trend in tissueoxygenation may be calculated, such as at block 1104, according to theone or more processes described in relation to FIG. 15.

Decision block 1106 may represent one or more processes executed byprocessor 702 to determine if the calculated tissue oxygenation trendfrom block 1104 represents a negative trend. Accordingly, if it isdetermined that the calculated tissue oxygenation trend is negative,flowchart 1100 may proceed to block 1108. In one implementation, upondetermining that data received from a tissue oxygenation sensor isrepresentative of a negative trend, one or more processes may beexecuted to output a signal indicating that the user may be exercisingat an unsustainable work rate. As such, one or more processes configuredto output a signal indicating that the user may be exercising at anunsustainable work rate may be executed at block 1108. In anotherimplementation, if it is determined that data received from a tissueoxygenation sensor is not representative of a negative trend, flowchart1100 may proceed to decision block 1110. Accordingly, decision block1110 may be associated with one or more processes executed to determinewhether the calculated tissue oxygenation trend is positive. If it isdetermined that the calculated tissue oxygenation trend is positive,flowchart 1100 may proceed to block 1112. Accordingly, in one example,if it is determined that the calculated tissue oxygenation trend ispositive, a signal may be outputted to indicate that the user isexercising at a sustainable work rate. In one example, the output signalindicating that the user is exercising at a sustainable work may beexecuted at block 1112 of flowchart 1100. In another example, if it isdetermined that the calculated tissue oxygenation trend is not positive,flowchart 1100 may proceed to block 1114. In this way, it may bedetermined that the calculated tissue oxygenation trend is approximatelylevel (unchanged). As such, a level tissue oxygenation trend may beindicative of a user exercising at a critical work rate. Accordingly, inresponse to determining that the tissue oxygenation trend isapproximately level, one or more processes may be executed to output asignal indicating that the user is exercising at a critical work rate.In one implementation, these one or more processes may be executed atblock 1114 of flowchart 1100.

It is noted that flowchart 1100 may calculate a tissue oxygenation trendfrom two or more data points indicative of muscle oxygenationpercentages at two or more different time points. As such, any numericalmethodology known in the art may be utilized to calculate a trendbetween two or more such points, including, among others, calculation ofa slope of a line connecting two points, or calculation of a regressionline using a plurality of points, among others.

FIG. 12 depicts two graphs of data generated during a same exercisesession. The two depicted graphs include muscle oxygenation percentagedata 1208 and running speed data 1210 plotted against a common timescale1206. In one implementation, the muscle oxygenation percentage data 1208may be generated by a muscle oxygenation sensor, such as sensor 710.Further, the running speed data 1210 may be calculated based on sensordata generated by sensor 706, which may include, among others, anaccelerometer, or a location-determining sensor. Accordingly, the graphof running speed 1210 may be associated with scale 1202, and the graphof muscle oxygenation 1208 may be associated with scale 1204. In oneexample, graphs 1208 and 1210 schematically depict relationships betweenmuscle oxygenation percentage and a running speed. In one example, theperiod between points 1218 and 1220 on the muscle oxygenation percentagegraph 1208 may represent a substantially level trend in muscleoxygenation percentage. Accordingly, points 1214 and 1216 on the speedgraph 1210 may correspond to points 1218 and 1220, and such that theapproximately level trend in muscle oxygenation percentage betweenpoints 1218 and 1220 corresponds to a critical speed, as schematicallyindicated by line 1212. In another example, a substantially negativetrend in muscle oxygenation percentage between points 1220 and 1224 onthe muscle oxygenation graph 1208 may correspond to an increase in speedabove the critical speed between points 1216 and 1222 on the speed graph1210. In yet another example, a positive trend in muscle oxygenationbetween points 1226 and 1228 on the muscle oxygenation graph 1208 maycorrespond to a decrease in speed below a critical speed between points1230 and 1232 on the speed graph 1210.

FIG. 13 is a flowchart diagram 1300 that may be utilized to determine ifa user is exercising within a severe exercise intensity domain. In oneimplementation, an activity monitoring device, such as device 700, mayreceive periodic data from a tissue oxygenation sensor, such as sensor710, indicative of a real-time oxygenation percentage of a tissue of auser while exercising. Accordingly, tissue oxygenation percentage datamay be received by, in one example, processor 702, from sensor 710, andwith a periodicity of one sample per second (1 Hz). In anotherimplementation, the sensor 710 may output a data point indicative of atissue oxygenation percentage once every two seconds (0.5 Hz), onceevery three seconds (0.33 Hz), once every four seconds (0.25 Hz), amongothers. Further, tissue oxygenation data may be generated by, andreceived from, the sensor 710 at any rate, without departing from thescope of these disclosures. In one example, one or more processes may beexecuted to receive, by a processor, such as processor 702, periodicdata from a tissue oxygenation sensor indicative of a tissue oxygenationpercentage at block 1302 of flowchart 1300.

In one implementation, tissue oxygenation data received from a tissueoxygenation sensor, such as sensor 710, may be stored in memory, such asmemory 704. Accordingly, in one example, a trend in tissue oxygenationdata may be calculated based upon a comparison of a mostrecently-received tissue oxygenation percentage data point, to one ormore previously-stored tissue oxygenation percentage data points. In oneexample, one or more processes may be executed by processor 702 of theactivity monitoring device 700 to calculate a change in tissueoxygenation percentage over a time spanning between a saved tissueoxygenation percentage data point, and a most recently-received tissueoxygenation percentage data point. In another implementation, one ormore processes may be executed by processor 702 of the activitymonitoring device 700 to calculate a trend in tissue oxygenationpercentage as a slope of a regression line calculated using two or moretissue oxygenation percentage data points. In one example, one or moreprocesses for calculation of a tissue oxygenation trend may be executedat block 1304 of flowchart 1300.

Decision block 1306 may represent one or more processes executed byprocessor 702 to determine if the calculated tissue oxygenation trendfrom block 1304 represents a negative trend. Accordingly, if it isdetermined that the calculated tissue oxygenation trend is negative,flowchart 1300 may proceed to decision block 1308. In oneimplementation, decision block 1308 may execute one or more processes tocalculate an absolute value of a negative trend (negative slope)identified at decision block 1306. Additionally, decision block 1308 mayrepresent one or more processes configured to compare the absolute valueof the negative trend to a threshold value. In one example, if theabsolute value is above a threshold value, flowchart 1300 may proceed toblock 1310. Accordingly, the threshold value may comprise any value,without departing from the scope of these disclosures. In one example,upon determining that the absolute value is above a threshold value, oneor more processes may be configured to output a signal indicating thatthe user is exercising in a severe intensity domain. As such, these oneor more processes configured to output a signal indicating that the useris exercising within a severe intensity domain may be executed at block1310. If, however, the absolute value is below a threshold, flowchart1300 may proceed to block 1312. Accordingly, block 1312 may comprise oneor more processes that may be executed to output a signal indicatingthat the user is exercising at an unsustainable work rate.

In another implementation, if it is determined that data received from atissue oxygenation sensor is not representative of a negative trend,flowchart 1300 may proceed to decision block 1314. Accordingly, decisionblock 1314 may be associated with one or more processes executed todetermine whether the calculated tissue oxygenation trend is positive.If it is determined that the calculated tissue oxygenation trend ispositive, flowchart 1300 may proceed to block 1316. Accordingly, in oneexample, if it is determined that the calculated tissue oxygenationtrend is positive, a signal may be outputted to indicate that the useris exercising at a sustainable work rate. In one example, the outputsignal indicating that the user is exercising at a sustainable work maybe executed at block 1316 of flowchart 1300. In another example, if itis determined that the calculated tissue oxygenation trend is notpositive, flowchart 1300 may proceed to block 1318. In this way, it maybe determined that the calculated tissue oxygenation trend isapproximately level (unchanged). As such, a level tissue oxygenationtrend may be indicative of a user exercising at a critical work rate.Accordingly, in response to determining that the tissue oxygenationtrend is approximately level, one or more processes may be executed tooutput a signal indicating that the user is exercising at a criticalwork rate. In one implementation, these one or more processes may beexecuted at block 1318 of flowchart 1300.

FIG. 14A depicts two graphs plotted using data from two separateexercise sessions participated in by a same user. In particular, graph1406 comprises output data from a ramped work rate exercise session. Inone example, work rate (W) may be depicted on the y-axis 1402.Accordingly, the data associated with graph 1406 may be generated fromdata outputted during an exercise session that prescribes alinearly-increasing work rate that increases from a work rate below acritical intensity, to a work rate above a critical intensity for theuser. Graph 1408 may be generated from data outputted during an exercisesession that prescribes a constant work rate. In one example, theconstant work rate associated with graph 1408 may be approximately 15%above a critical intensity for the user. Accordingly, the exercisesession associated with graph 1408 may be within a severe exerciseintensity domain for the user. In one example, the x-axis 1404represents a percentage of time to the end of an exercise session.Further, point 1410 represents an approximate time at which the rampedwork rate exercise session reaches the critical intensity for the user.

FIG. 14B depicts two graphs plotted using data from the same twoseparate exercise sessions from FIG. 14A. In particular, graph 1424 maycorrespond to the ramped work rate exercise session associated withgraph 1406. Additionally, graph 1426 may correspond to the constant workrate exercise session associated with graph 1408. In one example, graphs1424 and 1426 may be plotted as tissue oxygenation percentage on ay-axis 1420 versus percentage of time to an end of exercise session onan x-axis 1422. In one example, graphs 1406, 1408, 1424, and 1426 mayshare a common x-axis scale.

In one implementation, graph 1426, which is plotted using data from aconstant work rate exercise session with a constant work rate atapproximately 15% above a critical intensity for the user, may exhibit asteep slope between points 1432 and 1434 at the beginning of theexercise session (i.e. between approximately 0 and 20% of the time tothe end of the exercise session). However, a graph 1426 may transitionto a shallower slope between points 1434 and 1430 as the constant workrate exercise session is completed.

In one example, graph 1424, which may commence at a work rate below acritical intensity, may exhibit a shallower slope between points 1432and 1428. Accordingly, point 1428 may approximately correspond to apoint at which the ramped exercise intensity session associated withgraph 1406 reaches the critical intensity for the user (i.e. transitionsfrom a heavy to a severe exercise intensity domain for the user). Assuch, a slope of the graph 1424 may steepen between points 1428 and1430. Accordingly, in one example, a slope of graph 1424 between points1428 and 1430 may represent a slope with absolute value above athreshold value, said threshold value corresponding to a criticalintensity for the user. In one example, a slope of graphs 1424 betweenpoints 1428 and 1430 may be approximately equal to a slope of graph 1426between points 1432 and 1434.

FIG. 15 is a flowchart diagram 1500 that may be executed as one or moreprocesses, such as by device 700, to determine if received tissueoxygenation data represents exercise by a user at a critical intensity.In one example, tissue oxygenation data may be received from a sensor,such as sensor 710 associated with the device 700. As such, the tissueoxygenation data may correspond to muscle oxygenation, and may beexpressed as muscle oxygenation percentages. Accordingly, one or moreprocesses may be executed to receive tissue oxygenation data from asensor at block 1502 of process 1500. Further, tissue oxygenation datamay be received from a sensor, such as sensor 710, with any periodicity,or at non-periodic intervals, without departing from the scope of thesedisclosures. In one implementation, data received from a tissueoxygenation sensor at block 1502 may be stored in memory, such as memory704.

In one example, a change in tissue oxygenation may be calculated as adifference between a current rolling average and a previous rollingaverage of tissue oxygenation. Accordingly, a current rolling averagemay be calculated as an average value of tissue oxygenation percentageover a first duration, whereby the current rolling average may include amost-recently received sensor data point. In another implementation, acurrent rolling average may be calculated as an average value of tissueoxygenation percentage over a predetermined number of received sensordata points (which may be received with a periodicity, ornon-periodically). In certain specific examples, a current rollingaverage may be calculated as an average muscle oxygenation percentagefor those muscle oxygenation percentage data points received during thepast five seconds, including a most-recently received data point.However, alternative times for the first duration may be utilized,without departing from the scope of these disclosures. For example, thefirst duration may be one second, two seconds, three seconds, fourseconds, and six seconds, seven seconds, eight seconds, nine seconds,ten seconds, or any other duration. Further, the previous rollingaverage may be calculated as an average value of tissue oxygenationpercentage over a second duration, whereby the previous rolling averagedoes not include the most-recently received sensor data point (i.e. mayinclude at least all the data points used to calculate the currentrolling average, except the most-recently received sensor data point).In one example, the previous rolling average may be calculated for asecond duration, equal to the first duration. In one implementation, thedifference between the present rolling average, and the previous rollingaverage may be calculated by subtraction, thereby resulting in apercentage muscle oxygenation difference. In one implementation, one ormore processes utilized to calculate a change in tissue oxygenation maybe executed at block 1504 of process 1500, and by, in one example,processor 702.

In order to determine whether a calculated change in tissue oxygenationcorresponds to a critical tissue oxygenation (critical intensity) for agiven user, the calculated change in tissue oxygenation may be comparedto a threshold value. In one example, this threshold value of change intissue oxygenation percentage may include any oxygenation value. In onespecific example, the threshold value of change in tissue oxygenationpercentage may be less than 0.1 (i.e. the received tissue oxygenationpercentage may not correspond to a critical tissue oxygenationpercentage unless a difference between a current rolling average oftissue oxygenation percentage and a previous rolling average is lessthan 0.1 (units of tissue oxygenation percentage)). In another example,the received tissue oxygenation percentage may not correspond to acritical tissue oxygenation percentage unless a difference between acurrent rolling average of tissue oxygenation percentage and a previousrolling average is less than or equal to 0.1 (units of tissueoxygenation percentage). Additional or alternative tissue oxygenationthresholds may include 0.2, 0.3, 0.4, 0.5, 0.6 among others. In oneexample, one or more processes may be executed to determine whether achange in tissue oxygenation is less than a threshold at decision block1506 of process 1500.

If it is determined that a calculated change in tissue oxygenation isgreater than or equal to a threshold value (or in anotherimplementation, if it is determined that a calculated change in tissueoxygenation is greater than to a threshold value), one or more processesmay be executed to output a signal indicating that the user is notexercising at a critical tissue oxygenation. Accordingly, these one ormore processes may be executed at block 1510, and by a processor, suchas processor 702. If, however, it is determined that the calculatedchange in tissue oxygenation is less than a threshold value (or inanother implementation, less than or equal to the threshold value),flowchart 1500 may proceed to decision block 1508.

Accordingly, decision block 1508 may represent one or more processesexecuted to determine whether the change in tissue oxygenation (that isless than the previously-described threshold value, or in anotherimplementation, less than or equal to the threshold value) isconsistent/steady for a threshold duration (i.e. that the change intissue oxygenation percentage is less than a threshold change, and for apredetermined threshold duration). Accordingly, any threshold durationmay be utilized with these disclosures. In certain specific examples,the threshold duration may be equal to at least one second, at least twoseconds, at least three seconds, at least four seconds, at least fiveseconds, or at least 10 seconds, among others. If it is determined thatthe calculated change in tissue oxygenation is not consistent for thethreshold duration, flowchart 1500 may proceed to block 1510. If,however, it is determined that the calculated change in tissueoxygenation is consistent for the threshold duration, flowchart 1500 mayproceed to block 1512.

As such, upon determining that the calculated change in tissueoxygenation is consistent for a threshold duration, one or moreprocesses may be executed to output a signal indicating that a user isexercising at a critical work rate/critical tissue oxygenation (whichmay be expressed as a tissue oxygenation percentage). These one or moreprocesses configured to output a signal indicating that the user isexercising at a critical work rate may be executed by processor 702.Further, one or more processes may be executed at block 1512 to output atissue oxygenation percentage corresponding to those received sensorvalues for which the difference between the current rolling average andprevious rolling average is less than the threshold. This tissueoxygenation percentage may be a critical tissue oxygenation percentagefor the user.

In one example, there may be variation in a critical muscle oxygenationpercentage calculated for a user during different times of a sameexercise. Accordingly, in one example, the critical tissue oxygenationpercentage outputted at block 1512 may be averaged across multipleseparately-calculated critical tissue oxygenation percentages for a sameuser during an exercise session, among others.

In one implementation, the tissue oxygenation discussed in relation toflowchart 1500, as well as throughout this disclosure, may comprise amuscle oxygenation for any muscle within a user's body. Further, thiscalculated critical tissue oxygenation percentage value may be utilizedto calculate an anaerobic work capacity (M′) for the user (in oneexample, this anaerobic work capacity may be expressed as a total numberof muscle oxygenation points), and calculated as a difference between acurrent muscle oxygenation percentage (MO_(2 current)) (above thecritical muscle oxygenation percentage) and the critical muscleoxygenation percentage (MO_(2 crit)), summed over a duration of anexercise session to fatigue:

M′=Σ _(t=0) ^(fatigue)(MO_(2 current)−MO_(2 crit))(Units: muscleoxygenation points); (First anaerobic work capacity equation)

FIG. 16 depicts a graph 1602 of muscle oxygenation percentage (%)plotted on the y-axis 1604 versus duration (time) (s) on the x-axis1606. In one example, graph 1602 comprises data points 1608, 1610, 1612,and 1614, wherein data points 1608, 1610, 1612, and 1614 representseparate exercise sessions. As such, a data point, from data points1608, 1610, 1612, and 1614, may be associated with a total time of anexercise session, and a muscle oxygenation percentage associated withthat exercise session. In one example, this muscle oxygenationpercentage may be an average muscle oxygenation over the total time ofthe exercise session. In another example, this muscle oxygenationpercentage may be a muscle oxygenation percentage at the end of theexercise session, among others. In one implementation, graph 1602displays a trend in muscle oxygenation percentage for different exercisesession durations. In particular, graph 1602 may indicate that acomparatively shorter exercise session, such as that exercise sessionassociated with data point 1614, may be associated with a lower muscleoxygenation percentage. This trend may be due to a user exercising at acomparatively higher work rate for a comparatively shorter time. Incontrast to a data point 1614, data point 1608 may be associated with acomparatively longer exercise session, and may be associated with ahigher muscle oxygenation percentage as a result of a user exercisingfor a comparatively longer time, and adopting, in one example, a lessstrenuous pacing strategy in order to conserve energy for thecomparatively longer exercise session duration. In one example, graph1602 may comprise a curvilinear regression plotted through data points1608, 1610, 1612, and 1614. As such, any processes known in the art maybe utilized to construct graph 1610, without departing from thesedisclosures.

FIG. 17 depicts two graphs of data generated during a same exercisesession. The two depicted graphs include muscle oxygenation percentagedata 1702 and running speed data 1704 plotted against a common timescale1706. In one implementation, the muscle oxygenation percentage data 1702may be generated by a muscle oxygenation sensor, such as sensor 710.Further, the running speed data 1704 may be calculated based on sensordata generated by sensor 706, which may include, among others, anaccelerometer, or a location-determining sensor. Accordingly, the graphof running speed 1702 may be associated with scale 1708, and the graphof muscle oxygenation 1704 may be associated with scale 1710. In oneexample, graphs 1702 and 1704 schematically depict relationships betweenmuscle oxygenation percentage and a running speed. In oneimplementation, given the critical intensity (critical running speed)denoted by line 1712, and the critical intensity (critical muscleoxygenation percentage) denoted by line 1714, a relationship between thespeed 1702, and the muscle oxygenation percentage 1704 may berecognized. In particular, when a user's speed is below a criticalspeed, such as within shaded area 1716, a corresponding muscleoxygenation percentage for the user will be above a critical muscleoxygenation percentage 1714, such as within that shaded area 1718, andvice versa.

FIG. 18 depicts a graph 1802 of power on a y-axis 1804 versus time onthe x-axis 1806 for an exercise session. As will be readily appreciated,the data associated with FIG. 18 may be derived from any exercise/sporttype, without departing from the scope of these disclosures. Forexample, the graph 1802 may comprise data outputted from a power sensorduring a running session, cycling session, tennis game, basketball game,or soccer game, among others. In one example, graph 1802 may comprisepower data received by a processor, such as processor 702 of activitymonitoring device 700. As such, the activity monitoring device 700 maycomprise, or may be configured to communicate with, a power sensor fromwhich power data is directly outputted, or from which power values maybe calculated. Accordingly, as described herein, a power sensor maycomprise an accelerometer from which acceleration data input may beutilized to calculate a user's speed, and further, a user's rate ofenergy consumption (power). In another example, a power sensor maycomprise a dynamometer that may be operatively coupled to an exercisebike on which a user is exercising, among others.

In one example, the data points 1808, 1810, and 1812 may representcalculated critical power values for the user. Accordingly, in oneexample, these critical power values may be calculated using one or moreprocesses described in relation to FIG. 19. As such, FIG. 19 depicts aflowchart diagram 1900 that may be executed by activity monitoringdevice 700. In one implementation, flowchart diagram 1900 may beutilized to calculate a critical power associated with an exercisesession undertaken by a user. Further, this exercise session maycomprise at least a portion undertaken within a severe exerciseintensity domain. In one example, flowchart diagram 1900 may utilize atissue oxygenation sensor, such as sensor 710, and a power sensor, whichmay comprise one or more of a dynamometer, or an accelerometer, amongothers. In one example, the tissue oxygenation sensor may be configuredto output data indicative of a tissue oxygenation percentage with aperiodicity, or at a non-periodic rate. Accordingly, a periodicity withwhich the tissue oxygenation sensor outputs data points indicative of atissue oxygenation percentage may have any value, without departing fromthe scope of these disclosures. Further, the activity monitoring device700 may execute one or more processes to receive tissue oxygenation dataat block 1902 of flowchart 1900. The activity monitoring device 700 may,in one example, execute one or more processes to receive power data froma sensor, at block 1903 of flowchart 1900.

A change in tissue oxygenation may be calculated as a difference betweena current tissue oxygenation value and a previous tissue oxygenationvalue. Accordingly, in one implementation, the current tissueoxygenation value may correspond to a rolling average, and similarly,the previous tissue oxygenation value may correspond to a previousrolling average of tissue oxygenation. As such, the current rollingaverage may be calculated as an average value of tissue oxygenationpercentage over a first duration, whereby the current rolling averagemay include a most-recently received sensor data point. In anotherimplementation, a current rolling average may be calculated as anaverage value of tissue oxygenation percentage over a predeterminednumber of received sensor data points (which may be received with aperiodicity, or at a non-periodic rate). In certain specific examples, acurrent rolling average may be calculated as an average muscleoxygenation percentage for those muscle oxygenation percentage datapoints received during the past five seconds, including a most-recentlyreceived data point. However, alternative times for this first durationmay be utilized, without departing from the scope of these disclosures.For example, the first duration may be at least one second, two seconds,three seconds, four seconds, and six seconds, seven seconds, eightseconds, nine seconds, ten seconds. In another example, the firstduration may range between zero and one seconds, one and three seconds,two and six seconds, or five and ten seconds, or any other duration ortime range.

In one example, the previous rolling average may be calculated as anaverage value of tissue oxygenation percentage over a second duration,whereby the previous rolling average may not include the most-recentlyreceived sensor data point (i.e. may include at least all the datapoints used to calculate the current rolling average, except themost-recently received sensor data point). In one example, the previousrolling average may be calculated for a second duration, equal to thefirst duration. In one implementation, the difference between thecurrent rolling average, and the previous rolling average may becalculated by subtraction, thereby resulting in a percentage muscleoxygenation difference. In one implementation, one or more processesutilized to calculate a change in tissue oxygenation may be executed atblock 1904 of process 1900, and by, in one example, processor 702.

In one implementation, in order to determine whether a calculated changein tissue oxygenation corresponds to a critical tissue oxygenation(critical intensity) for a given user, the calculated change in tissueoxygenation may be compared to a threshold value. In one example, thisthreshold value of change in tissue oxygenation percentage may includeany oxygenation value. In one specific example, the threshold value ofchange in tissue oxygenation percentage may be less than or equal to 0.1(i.e. the received tissue oxygenation percentage may not correspond to acritical tissue oxygenation percentage unless a difference between acurrent rolling average of tissue oxygenation percentage and a previousrolling average is less than or equal to 0.1 (units of tissueoxygenation percentage)). Additional or alternative tissue oxygenationthresholds may include, among others, 0.2, 0.3, 0.4, 0.5, 0.6. In oneexample, one or more processes may be executed to determine whether achange in tissue oxygenation is less than a threshold at decision block1906 of process 1900.

If it is determined that a calculated change in tissue oxygenation isgreater than a threshold value, one or more processes may be executed tooutput a signal (output to, in one example, an interface, such as agraphical user interface, or a wireless interface/transceiver)indicating that the user is not exercising at a critical tissueoxygenation. Accordingly, these one or more processes may be executed atblock 1910, and by a processor, such as processor 702. If, however, itis determined that the calculated change in tissue oxygenation is lessthan or equal to a threshold value, flowchart 1900 may proceed todecision block 1908.

Accordingly, decision block 1908 may represent one or more processesexecuted to determine whether the change in tissue oxygenation (that isless than or equal to the previously-described threshold value) isconsistent/steady for a threshold duration (i.e. that the change intissue oxygenation percentage is less than or equal to a thresholdchange, and for a predetermined threshold duration). Accordingly, anythreshold duration may be utilized with these disclosures. In certainspecific examples, the threshold duration may be equal to at least onesecond, at least two seconds, at least three seconds, at least fourseconds, at least five seconds, or at least 10 seconds, or range betweenapproximately 1 and 10 seconds, or 5 and 15 seconds, among others. If itis determined that the calculated change in tissue oxygenation is notconsistent for the threshold duration, flowchart 1900 may proceed toblock 1910. If, however, it is determined that the calculated change intissue oxygenation is consistent for the threshold duration, flowchart1900 may proceed to block 1912.

As such, upon determining that the calculated change in tissueoxygenation is consistent for a threshold duration, one or moreprocesses may be executed to output a signal indicating that a user isexercising at a critical work rate/critical tissue oxygenation.Specifically, in one example, one or more processes may be executed tooutput a critical power of the user equal to the current power asindicated by the power sensor at a time corresponding to the identifiedcritical tissue oxygenation. Alternatively, a critical power maycorrespond to an average power as indicated by the power sensor over atime period corresponding to the calculation of the calculatedconsistent change in tissue oxygenation. As such, these one or moreprocesses configured to output a critical power to an interface may beexecuted by processor 702. Further, one or more processes may beexecuted at block 1912 to output the critical power corresponding tothose received sensor values for which the difference between thecurrent rolling average and previous rolling average is less than orequal to the threshold.

In one example, there may be some degree of variation in acalculated/identified critical power for a user based upon multiplecalculations of the critical muscle oxygenation during different timesof a same exercise. Accordingly, in one example, the critical poweroutputted at block 1912 may be averaged across multipleseparately-calculated critical tissue oxygenation percentages for a sameuser during an exercise session, among others. Accordingly, in oneexample, the data points 1808, 1810, and 1812 may represent exemplarydata points from a plurality of critical power results corresponding tomultiple calculations of critical tissue oxygenation of the user. Assuch, in one example, the critical power values associated with datapoints 1808, 1810, and 1812 may be averaged.

In one implementation, the tissue oxygenation discussed in relation toflowchart 1900, as well as throughout this disclosure, may comprise amuscle oxygenation for any muscle within a user's body. Further, thecritical power value calculated at block 1912 may be utilized tocalculate an anaerobic work capacity (W′) for the user (in one example,this anaerobic work capacity may be expressed as a power (units: W)),and calculated as a difference between a current muscle oxygenationpercentage (Power_(current)) (above the critical muscle oxygenationpercentage) and the critical power (Power_(crit)), summed over aduration of an exercise session. As such, these calculated differencesmay be referred to as positive difference values. In one example, anexercise session may end in user fatigue. Accordingly, one or moreprocesses utilized to calculate an anaerobic work capacity may beexecuted by processor 702 at block 1914:

W′=Σ _(t=0) ^(fatigue)(Power_(current)−Power_(crit))(Units: W); (Secondanaerobic work capacity equation)

In certain examples, a critical velocity and an anaerobic work capacityfor a user may be calculated based upon sensor output data indicating,or used to calculate, a speed of the user. As such, a critical velocityand an anaerobic work capacity for a user may be calculated based uponsensor data received from, among others, an accelerometer, alocation-determining sensor, or a bicycle speedometer, and withoututilizing a tissue oxygenation sensor, as previously described. In oneimplementation, the present disclosure describes results of a pluralityof validation tests utilized to validate a relationship between speeddata and a critical intensity for a user. Accordingly, in one example,an end speed may be calculated in order to estimate a critical speed fora user.

FIG. 20 depicts a graph 2000 that may be utilized to calculate an endspeed of the user during an exercise session, and comprising speed onthe y-axis 2002 versus time on the x-axis 2004. The plotted data points,of which data points 2006, 2008, 2010 are an exemplary sub-set, comprisemeasurements of a speed of the user at a given time during a sameexercise session. In one example, the end speed, denoted by line 2012,may be calculated as an average of a sub-set of the plurality of datapoints that make up the graph 2000. In particular, the end speed 2012may be calculated as an average of those data points during a final 30seconds of the duration of the exercise session (e.g. an average ofthose data points between lines 2014 and 2016). However, an end speedmay be calculated as an average speed for different durations, such as,among others, a final 20 seconds, 10 seconds, or 5 seconds of anexercise session, or any other duration. In one example, graph 2000 mayrepresent data points associated with an exercise session having aprescribed duration. Accordingly, the prescribed duration may range from1 minute to 10 minutes. In one specific example, the exercise sessionassociated with graph 2000 may be a three minute all-out trial, wherebya user is instructed to exercise as a highest subjective intensity levelfor the prescribed duration (three minutes). Additional or alternativeexercise session prescriptions (times and/or intensity levels) may beutilized without departing from the scope of these disclosures.

In one example, based upon validation testing comparing calculated endspeeds for multiple users across multiple separate exercise sessions, arelationship between a calculated end speed and a critical speed for agiven user may was identified. In particular, for a plurality ofvalidation tests, 90% of a sample population of users were found to beable to sustain exercise for up to 15 minutes when exercising between 5%and 10% below a calculated end speed for a three minute all-out trial.Additionally, for the plurality of validation tests, 85% of the samplepopulation of users were found to be able to sustain exercise for up to20 minutes when exercised between 5% and 10% below the calculated endspeed for the three minute all-out trial. Accordingly, in one example, acritical velocity for a user may be estimated by reducing a calculatedend speed by 5 to 10% (e.g. calculating 90-95% of an end speed of auser). In one specific example, a critical velocity may be estimated fora user by calculating 92.5% of an end speed, among others.

In one implementation, an anaerobic work capacity, expressed as adistance, it may be calculated based upon a calculated end speed for auser. Accordingly, from a plurality of validation testing comparing adistance above end speed to an anaerobic work capacity for a given user,it was found that an anaerobic work capacity may be estimated byincreasing a calculated end speed by, in one example, 25% to 35%. Inanother specific example, an anaerobic work capacity may be estimated byincreasing the calculated end speed by 30%. Accordingly, in one example,the distance above end speed may be as that area 2018 from FIG. 20 (e.g.an integration of differences between speed data points and thecalculated end speed 2012 across the duration of the exercise sessionassociated with graph 2000).

FIG. 21 is a flowchart diagram 2100 that may be utilized to calculate acritical speed and an anaerobic work capacity for a user based uponsensor data indicative of a speed of the user. Accordingly, in oneexample, one or more processes associated with flowchart diagram 2100may be executed by an activity monitoring device, such as device 700. Itis noted that flowchart diagram may utilized a sensor, such as sensor706 of device 700, but may not utilize an oxygenation sensor, such assensor 710. In one example, the activity monitoring device 700 mayreceive sensor data from a sensor, such as sensor 706. The receivedsensor data may comprise data points indicative of a speed of the userat various time points during an exercise session. In another example,the received sensor data points may comprise data indicative of alocation of the user, and may be utilized to calculate a speed. In oneexample, data points may be received periodically, and with anyperiodicity, without departing from the scope of these disclosures. Thereceived data points may be associated with an exercise session having aprescribed duration and intensity. In particular, the exercise sessionmay comprise a three minute all-out trial that instructs a user toexercise as a highest subjective intensity for a three minute duration.In another example, a prescribed duration of 2 to 5 minutes maybeutilized. In other examples, alternative durations may be utilized,without departing from the scope of these disclosures. In one example,one or more processes may be executed to receive sensor data at block2102 of flowchart 2100.

An end speed may be calculated from received sensor data as an averagespeed of a sub-set of a plurality of sensor data points received duringan exercise session. In one specific example, an end speed may becalculated as an average speed during a last 30 seconds of theprescribed duration of the exercise session. However, alternativesub-sections of a prescribed duration of an exercise session may beutilized to calculate an end speed, without departing from the scope ofthese disclosures. In one example, one or more processes may beexecuted, such as by a processor 702 to calculate an end speed at block2104 of flowchart 2100.

A distance above the end speed may be calculated by summing differencesbetween instantaneous speeds (Speed_(current)) and the calculated endspeed (Speed_(end)) across the duration of the exercise session (i.e.between time t=0 and the end of the session, time t=session end). In oneimplementation, one or more processes may be executed to calculate adistance above an end speed at block 2106 of flowchart 2100:

Distance above end speed=Σ_(t=0)^(session end)(Speed_(current)−Speed_(end))(Units: m); (Distance aboveend speed equation)

In one example, a critical speed may be calculated/estimated based uponthe calculated end speed. In one implementation, a critical speed may becalculated by decreasing the calculated end speed by between 5 and 10%:

Speed_(critical)=Speed_(end)*(90-95%).

In one specific example, a critical speed may be calculated as 92.5% ofa calculated end speed:

Speed_(critical)=Speed_(end)*(92.5%).

In one implementation, one or more processes may be executed tocalculate a critical speed, based upon the calculated end speed, atblock 2108 of flowchart 2100.

In one example, an anaerobic work capacity may be calculated based uponthe calculated distance above an end speed. Accordingly, the anaerobicwork capacity may be calculated as 125% to 135% of a distance above thecalculated end speed:

Anaerobic work capacity=(distance above end speed)*(125-135%).

In one specific example, an anaerobic work capacity may be calculated as130% of a distance above the calculated end speed:

Anaerobic work capacity=(distance above end speed)*(130%).

In one implementation, one or more processes may be executed tocalculate the anaerobic work capacity, based upon the calculateddistance above the end speed, at block 2110 of flowchart 2100.

In one implementation, a critical speed and an anaerobic work capacityassociated with a user may be calculated from data received from asensor configured to output data indicative of a distance traveled bythe user during an exercise session (e.g. distance traveled whilerunning, cycling, and the like). As such, the sensor may comprise one ormore of an accelerometer, a location determining sensor, or a bicyclespeedometer, among others. As such, the sensor may be configured tooutput data indicative of a location of a user, which may in turn beused to calculate a distance traveled by the user, as well as todetermine a time taken to travel the recorded distance. Accordingly,FIG. 22 schematically depicts a flowchart diagram 2200 that may beutilized to calculate one or more of a critical speed and/or ananaerobic work capacity of a user from data outputted from a sensor,such as sensor 706.

In one implementation, in order to calculate one or more of a criticalspeed and/or an anaerobic work capacity, a user may provide an activitymonitoring device, such as device 700, with test data. In one example,test data may be generated by a sensor, such as sensor 706, during anexercise period, which may otherwise be referred to as an exercisesession. In one example, an exercise period may comprise a prescribedduration during which a user is instructed to run as quickly as possible(i.e. as far as possible) within the prescribed time limit. In certainspecific examples, an exercise period may instruct a user to run as faras possible within, for example, a minute, two minutes, three minutes,four minutes, five minutes, six minutes, seven minutes, eight minutes,nine minutes, 10 minutes, 12 minutes, 15 minutes, 20 minutes, or anyother duration. Accordingly, a sensor, such as sensor 706, may beconfigured to output a location of the user for each second of anexercise period. In turn, this location data may be utilized tocalculate a total distance travelled by the user during the exerciseperiod. Alternatively, the sensor 706, may be configured to a locationdata point at a different frequency, which may be 0.25 Hz, 0.5 Hz, 2 Hz,3 Hz, 4 Hz, or any other frequency. In one example, an exercise periodprescribed for a user in order to generate test data may ensure that theuser exercises at an intensity above a critical intensity for the userduring at least a portion of the prescribed duration of the exerciseperiod. Accordingly, in one example, one or more processes may beexecuted to instruct a user to begin an exercise period at block 2202 offlowchart 2200.

In one example, the sensor 706 may output a data point indicative of acurrent location and/or a distance traveled by the user for each secondof an exercise period. Accordingly, in one example, the outputted datamay be received for further processing by, in one example, processor702, at block 2204 of flowchart 2200.

In one implementation, a data point associated with each second of aprescribed exercise period may be stored. As such, location data foreach second of a prescribed exercise period may be stored in, forexample, memory 704. Upon completion of a given exercise period, a totaldistance traveled during a prescribed exercise period may be calculated.In one implementation, this total distance traveled may be calculatedby, in one example processor 702, and at block 2206 of flowchart 2200.

In one example, an exercise period utilized to generate data in order todetermine a critical speed and/or an anaerobic work capacity of the usermay be summarized as a data point comprising two pieces of information.This exercise period summary data point may comprise the total distancetraveled, as determined, in one example, at block 2206, in addition tothe total time (i.e. the duration) of the exercise period/session. Inone example, the two pieces of information (i.e. the total distance, andthe total time) may be expressed as a coordinate point. In one example,this coordinate point P may be of the form P(x₃, y₃), where y₃ may bethe total distance (m), and x₃ may be the total time (s). In this way,the exercise period summary data point expressed as a coordinate pointmay be plotted, as schematically depicted in FIG. 23. In one example,the exercise period summary data point may be calculated at block 2208of flowchart 2200.

In one implementation, in order to calculate one or more of a criticalspeed and/or an anaerobic work capacity of a user, two or more exerciseperiod summary data points may be utilized. In one example, thedurations of the exercise periods used to generate the two or moreexercise period summary data points may be different. Accordingly, inone example, one or more processes may be executed to determine whethera threshold number of exercise periods have been completed by the userin order to calculate one or more of a critical speed and/or ananaerobic work capacity for the user. As previously described, thisthreshold number of exercise periods may be at least two, at leastthree, at least four, or at least five, among others. In one specificexample, one or more processes may be executed by processor 702 todetermine whether the threshold number of exercise periods have beencompleted at decision block 2210 of flowchart 2200. Accordingly, if thethreshold number of exercise periods has been met or exceeded, flowchart2200 proceeds to block 2212. If, however, the threshold number ofexercise periods has not been met, flowchart 2200 proceeds from decisionblock 2210 back to block 2202.

In one example, a regression may be calculated using the two or moreexercise period summary data points that were calculated from two ormore prescribed exercise periods. In one example, this regression may bea linear, or a curvilinear regression. As such, any computationalprocesses known in the art for calculation of a linear or curvilinearregression may be utilized with this disclosure. In one implementation,at least a portion of a calculated regression may be utilized todetermine one or more of a critical tissue oxygenation percentage and/oran anaerobic work capacity of the user. In one specific example, one ormore processes may be executed to calculate a regression at block 2212of flowchart 2200.

In one example, at least a portion of a regression calculated using thetwo or more exercise period summary data points may be utilized todetermine a critical speed for a user. Specifically, the critical speedmay correspond to a slope of the regression line (or a slope of a linearportion of a curvilinear regression). In one implementation, one or moreprocesses may be executed to output a critical speed calculated as aslope of a regression line through the two or more exercise periodsummary data points at block 2214 of flowchart 2200.

In another example, at least a portion of a regression calculated usingthe two or more exercise period summary data points may be utilized todetermine an anaerobic capacity for of the user. Specifically, theanaerobic capacity may correspond to an intercept of the regression line(or an intercept of a linear portion of a curvilinear regression). Inone example, the anaerobic capacity may be expressed as a total distance(m) above a critical speed (m/s). In one implementation, one or moreprocesses may be executed to output an anaerobic capacity calculated asan intercept of a regression line through the two or more exerciseperiod summary data points, at block 2216 of flowchart 2200.

FIG. 23 is a chart that plots testing data from multiple exercisesessions for a given user. In particular, FIG. 23 is a chart 2300plotting distance 2302 against time 2304. Points 2306, 2308, 2310, and2312 may each represent a separate exercise sessions, and such that atleast a portion of each of these exercise sessions is carried out withina severe exercise intensity domain for the user. In one implementation,the exercise period summary data points 2306, 2308, 2310, and 2312 mayeach represent a separate exercise session carried out in a continuousmanner. However, in another implementation, one or more of the exerciseperiod summary data points 2306, 2308, 2310, and 2312 may represent aseparate exercise session carried out in an intermittent manner.

In one implementation, a regression line 2314 may be calculated usingthe four exercise period summary data points 2306, 2308, 2310, and 2312,as plotted on chart 2300. In one example, this regression line 2314 maybe of the form:

y=m _(d) x+c _(d)

where y is a total distance (y-axis), x is a time (s) (x-axis), m_(d) isthe slope of the regression line 2314 and c_(d) is the intercept of theregression line 2314 on the y-axis.

For the example experimental data used to generate the exercise periodsummary data points 2306, 2308, 2310, and 2312, the regression line 2314may have the form: y=4.21x+181.96, with an r² value of 0.99979. It isnoted that this regression line 2314 formula is merely included as oneexample result.

In one example, a regression line, such as regression line 2314, throughtwo or more exercise period summary data points, such as the exerciseperiod summary data points 2306, 2308, 2310, and 2312, may be used tocalculate a critical speed and/or a total distance above a criticalspeed (D′) (which may be proportional to an anaerobic work capacity) fora user. In one example, given regression line 2314 of the form: y=mx+c,the critical speed may be equal to m, the slope of the regression line2314, and the total distance above the critical speed may be equal to c(or |c|, the absolute value of c), the intercept of the regression line2314 on the y-axis. In particular, given the experimental data depictedin chart 2300, the critical speed for the user may be 4.21 m/s and thetotal distance above the critical speed may be 181.96 m.

In certain examples, a critical velocity, a critical power, and/or ananaerobic work capacity may be calculated based upon a single input datapoint. In one implementation, this single input data point may comprisea race time (comprising a distance and a time taken to complete the racedistance). In one example, a race time may be utilized based upon anassumption that at least a portion of the race was carried out within asevere exercise intensity domain. However, a single input data pointcomprising a distance completed and a time taken to complete thedistance derived from an exercise session other than a race (i.e. aninformal running session untaken by a user) may be utilized with thesystems and methods described herein. In another implementation, asingle input data point may be utilized to calculate a critical powerand/or an anaerobic work capacity, and such that the single input datapoint may comprise the total amount of work done, and a total timetaken.

In one implementation, a single input data point may be utilized tocalculate one or more of a critical velocity, critical power, and/or ananaerobic work capacity based upon relationships (models) developedthrough analysis of multiple exercise sessions by multiple differentusers. In particular, FIG. 24 depicts a model 2402 that may be utilizedto predict a fraction of a critical velocity (y-axis 2404) based upon aninput of a total athletic session time (x-axis 2406) for running. Thedata points 2408, 2410, and 2412 are exemplary data points from aplurality of data points that may be utilized to develop the model 2402.Accordingly, data points 2408, 2410, and 2412 may represent separateexercise sessions (for a running exercise session) by a same user, or bydifferent users. In one example, model 2402 may be of the form:y=1.8677*x^(−0.082), with an r² value of 0.6816. In another example,model 2402 may be of the form: y=1.87*x^(−0.1).

FIG. 25 depicts a model 2502 that may be utilized to predict a fractionof a critical velocity (y-axis 2504) based upon an input of a totalathletic session distance (x-axis 2506) for a running exercise session.The data points 2508, 2510, and 2512 are exemplary data points from aplurality of data points that may be utilized to develop the model 2502.Accordingly, data points 2508, 2510, and 2512 may represent separateexercise sessions (for a running exercise session) by a same user, or bydifferent users. In one example, model 2502 may be of the form:y=2.2398*x^(−0.09), with an r² value of 0.6779. In another example,model 2502 may be of the form: y=2.2*x^(−0.1)

FIG. 26 depicts a model 2602 that may be utilized to predict a fractionof a critical velocity (y-axis 2604) based upon an input of a totalathletic session time (x-axis 2606) for cycling. The data points 2608,2610, and 2612 are exemplary data points from a plurality of data pointsthat may be utilized to develop the model 2602. Accordingly, data points2608, 2610, and 2612 may represent separate exercise sessions (for acycling exercise session) by a same user, or by different users. In oneexample, model 2602 may be of the form: y=1.9199*x^(−0.088), with an r²value of 0.8053. In another example, model 2602 may be of the form:y=1.9*x^(−0.1)

FIG. 27 depicts a model 2702 that may be utilized to predict a fractionof a critical velocity (y-axis 2704) based upon an input of a totalamount of energy expended during an athletic session (x-axis 2706) forcycling. The data points 2708, 2710, and 2712 are exemplary data pointsfrom a plurality of data points that may be utilized to develop themodel 2702. Accordingly, data points 2708, 2710, and 2712 may representseparate exercise sessions (for a cycling exercise session) by a sameuser, or by different users. In one example, model 2702 may be of theform: y=3.0889*x^(−0.086), with an r² value of 0.6769. In anotherexample, model 2702 may be of the form: y=3.1*x^(−0.1)

In one implementation, models 2402, 2502, 2602, and/or 2702 may becalculated using any mathematical modeling methodology known in the art(e.g. regression modeling methodologies, among others).

FIG. 28 is a flowchart diagram 2800 that may be utilized to calculateone or more of a critical velocity (or critical power) and an anaerobicwork capacity based upon a single input data point. Accordingly, the oneor more processes associated with flowchart 2800 may be executed by aprocessor, such as processor 702. In one example, a single input datapoint may comprise a total time in combination with a total distance foran exercise session. In one example, at least a portion of the exercisesession may be carried out within a severe exercise intensity domain. Inanother example, a single input data point may comprise a total powerexpended and a total time associated with an exercise session.Accordingly, one or more processes executed to receive the single inputdata point may be executed at block 2802. In one specific example, adata point may indicate that a user completed a 5 km race in 1300seconds.

A mathematical model may be utilized to calculate a critical velocityfraction or a critical power fraction. Accordingly, an input to a model,from, in one example, models 2402, 2502, 2602, and/or 2702, may comprisea total distance traveled during an exercise session, a total time tocomplete an exercise session, or a total power expended during anexercise session. Further, the selection of a model, from, in oneexample, models 2402, 2502, 2602, and/or 2702, may be based upon anactivity type (e.g. running or cycling, among others). In oneimplementation, one or more processes may be executed to calculate acritical velocity fraction or a critical power fraction at block 2804 offlowchart 2800. For the specific example of a 5 km race run completed in1300 seconds, the critical velocity fraction may be calculated asy=1.8677*(1300)^(−0.082) (model 2402), which implies that the criticalvelocity fraction (y)=1.045.

Additionally, an average velocity may be calculated as a total exercisesession distance divided by a total time taken to complete the distance.In another implementation, an average exercise session power may becalculated as a total exercise session power divided by a total timetaken to complete the exercise. Accordingly, one or more processes maybe executed to calculate an average exercise session velocity, or anaverage exercise session power, at block 2806 of flowchart 2800. For thespecific example of a 5 km race run in 1300 seconds, the averageexercise session velocity may be 5000/1300=3.85 m/s.

A critical velocity may be calculated as an average velocity divided bythe critical velocity fraction. Alternatively a critical power for auser may be calculated as an average power divided by the critical powerfraction. Accordingly, one or more processes may be executed tocalculate a critical velocity, or a critical power, at block 2808 offlowchart 2800. For the specific example of a 5 km race run in 1300seconds, the critical velocity may be calculated as 3.85/1.045=3.68 m/s.

A total distance traveled below a critical velocity may be calculated asan average velocity multiplied by a total time associated with anexercise session. Alternatively, the total amount of energy expendedbelow a critical power may be calculated as an average power multipliedby a total time associated with an exercise session. Accordingly, one ormore processes may be executed to calculate a total distance traveledbelow a critical velocity, or a total amount of energy expended below acritical power, at block 2810 of flowchart 2800. For the specificexample of a 5 km race run in 1300 seconds, the distance traveled belowthe critical velocity may be calculated as 3.68*1300=4784 m.

An anaerobic work capacity may be calculated as a distance above acritical velocity, or as a total amount of energy above a criticalpower. Accordingly, an anaerobic work capacity may be calculated (e.g.for running) as a difference between a total distance traveled during anexercise session and a total distance traveled below a criticalvelocity, as calculated at block 2810. Alternatively, an anaerobic workcapacity may be calculated (e.g. for cycling) as a difference between atotal amount of energy expended during an exercise session and a totalamount of energy expended below a critical power, as calculated at block2810. Accordingly, one or more processes may be executed to calculate ananaerobic work capacity at block 2812 of flowchart 2800. For thespecific example of a 5 km race run in 1300 seconds, the distancetraveled above the critical velocity (i.e. the anaerobic work capacity,D′) may be equal to 5000−4784=216 m.

In certain implementations, a volume of oxygen consumption associatedwith an exercise session participated in by a user may be estimatedwithout using any sensors. In particular, a volume of oxygen consumptionof the user may be estimated based upon an athletic profile constructedusing one or more questions answered by the user. This questionnaire maybe administered to the user in an electronic format, and may compriseone or more questions. In one example, answers to these questions may bebased on a scale. In one example, the scale may comprise numbers from 0to 10. However, additional or alternative scales may be utilized,without departing from the scope of these disclosures. Specifically, thequestions may include, among others: an estimation of bone size, anestimation of leanness of the user, an estimation of muscle size, anestimation of sleep quality, an estimation of relaxation habits, anestimation of nutrition quality, an estimation of smoking status, anestimation of drinking habits, and an estimation of an activeness of theuser. Additional or alternative questionnaire questions utilized toconstruct an athletic profile for the user may include, a user's age,gender, height, waist circumference, weight, as well as an indication asto whether the user is pregnant. Still further questionnaire questionsmay include an estimation of a 5 km running race pace (or a paceassociated with another distance), and an estimation of a number of daysactive during the week.

FIG. 29 depicts a flowchart diagram 2900 that may be utilized toestimate a volume of oxygen consumption of the user in response to areceived rate of perceived exertion of the user, and using an athleticprofile constructed using one or more questionnaire questions. Inparticular, a user may be asked to respond to one or more questionnairequestions, which may include one or more of those questions describedabove. Accordingly, one or more processes may be executed by aprocessor, such as processor 702 to receive one or more questionnaireresponses at block 2902 of flowchart 2900.

An athletic profile may be calculated and stored, such as within memory704, based upon one or more of the received questionnaire responses.Accordingly, this athletic profile may account for one or more physicaland/or behavioral attributes associated with user, which may impact avolume of oxygen consumption for the user. In one example, the athleticprofile may estimate a maximal volume of oxygen consumption associatedwith user, based upon one or more physical and/or behavioral attributesof the user. Accordingly, one or more processes may be executed tocalculate and store the athletic profile at block 2904.

In one example, a user may input a rate of perceived exertion followingan exercise session. This rate of perceived exertion may be received bya processor, such as processor 702 via an interface, such as interface708. In one example, the rate of perceived exertion may be received as anumber on a scale of 0 to 10. However, those of ordinary skill in theart will recognize that additional or alternative scales may be utilizedwith his rate of perceived exertion, without departing from the scope ofthese disclosures. In one example, the rate of perceived exertion may bereceived from the user at block 2906.

The received rate of perceived exertion may be mapped to an oxygenconsumption scale for the user, based upon the constructed athleticprofile for the user. In one example, the scale of the rate of perceivedexertion may be linearly mapped to a volume of oxygen consumption scaledelimited by a maximal oxygen consumption estimated for the user basedupon the calculated athletic profile for the user. In otherimplementations, nonlinear mappings of the rate of perceived exertionscale to the volume of oxygen consumption scale may be utilized, withoutdeparting from the scope of these disclosures. Accordingly, one or moreprocesses to map the received rate of perceived exertion to the oxygenconsumption scale may be executed at block 2908. Additionally, one ormore processes may be executed to output an estimated volume of oxygenconsumption, based upon the inputted rate of perceived exertion of theuser, at block 2910.

In one example, an anaerobic work capacity of the user may bereplenished when the user exercises at an intensity that is below acritical intensity (i.e. within a moderate or heavy exercise intensitydomain). As previously described, an anaerobic work capacity may beexpressed as a total number of muscle oxygenation points, as derivedfrom muscle oxygenation sensor data, such as data outputted byoxygenation sensor 710. As such, the anaerobic work capacity may bedenoted M′. In one example, replenishment of anaerobic work capacity maybe denoted M′ rate and calculated as a difference between a currentmuscle oxygenation percentage and a critical muscle oxygenationpercentage: M′_rate=% MO2−critical % MO2. In one example, the M′_ratemay be continuously summed throughout a duration of an exerciseperiod/trial in order to determine an M′ balance (i.e. a total number ofmuscle oxygenation points). Accordingly, when a current muscleoxygenation percentage is below the critical muscle oxygenationpercentage, the calculated M′ rate may be negative, and indicative of afinite work capacity (anaerobic work capacity) being consumed. Further,when a current muscle oxygenation percentage is above the criticalmuscle oxygenation percentage for a user, the M′ rate may be positive,and the finite work capacity may be replenished. FIG. 30 schematicallydepicts this consumption and replenishment of total muscle oxygenationpoints. In particular, FIG. 30 graphs M′ replenishment on the y-axis3002 for four different exercise intensity domains (i.e. severe 3004,heavy 3006, moderate 3008 and rest 3010). As such, FIG. 30 schematicallydemonstrates that an M′_rate associated with a severe exercise intensitydomain may be negative, but the anaerobic work capacity of the user maybe replenished as the user transitions to exercise within a heavyexercise intensity domain 3006, in a moderate exercise intensity domain3008, and when the user is at rest 3010.

Various systems and methods are described in this disclosure forcalculation of a critical intensity (critical velocity, or a criticalpower) for a user. Additionally, various systems and methods aredescribed in this disclosure for calculation of an anaerobic workcapacity/finite work capacity (M′, D′) associated with user. As such,given these calculated critical intensity and finite work capacityvalues, various activity metrics may be predicted. As such, those ofordinary skill in the art will recognize various methodologies that maybe utilized predict athletic metrics using one or more of a criticalintensity and a finite work capacity for a user, without departing fromthe scope of these disclosures. In one example a prediction of a time, t(s), to completion of an athletic event (e.g. a race), given a presentvelocity, v_(p) (m/s), a critical velocity, v_(crit) (m/s), and a finitework capacity, D′ (m), may be given by: t=D′/(v_(p)−v_(crit)).

For the avoidance of doubt, the present application extends to thesubject-matter described in the following numbered paragraphs (referredto as “Para” or “Paras”):

-   1. An apparatus, comprising:

a processor;

a user interface;

an oxygenation sensor, configured to be positioned proximate an area ofskin of a user, the oxygenation sensor further configured to output dataindicative of a tissue oxygenation of a body tissue of the user; and

a non-transitory computer-readable medium comprising computer-executableinstructions that when executed by the processor are configured toperform at least:

-   -   receive tissue oxygenation data from the oxygenation sensor        associated with at least two exercise periods, at least a        portion of each of the at least two exercise periods associated        with a severe exercise intensity domain, and the at least two        exercise periods having differing durations;    -   calculate a total number of tissue oxygenation points for each        of the at least two exercise periods;    -   calculate an exercise period summary data point for each of the        at least two exercise periods as a total number of tissue        oxygenation points versus a duration of exercise;    -   calculate a regression through the exercise period summary data        points for the at least two exercise periods;    -   output a critical tissue oxygenation percentage for the user        equal to a slope of at least a portion of the regression, and/or        output an anaerobic work capacity equal to an intercept        associated with at least a portion of the regression.

-   2. An apparatus according to Para 1, wherein the total number of    tissue oxygenation points for each of the at least two exercise    periods are calculated as an integration of tissue oxygenation    percentages for each second of a duration of an exercise period,    from the at least two exercise periods.

-   3. The apparatus of Para 1 or 2, wherein the computer-readable    instructions, when executed by the processor, further cause the    apparatus to:

receive data, from the oxygenation sensor, indicating an additionaltissue oxygenation percentage associated with an additional exerciseperiod; and

compare the additional tissue oxygenation percentage to the criticaltissue oxygenation percentage,

wherein:

if the additional tissue oxygenation percentage is less than thecritical tissue oxygenation, output, to the user interface, a signalindicating that the user is exercising at an unsustainable work rate,and/or

if the additional tissue oxygenation percentage is greater than or equalto the critical tissue oxygenation, output, to the user interface, asignal indicating that the user is exercising at a sustainable workrate.

-   4. The apparatus of any of the preceding Paras, wherein the    computer-readable instructions, when executed by the processor,    further cause the apparatus to:

receive data, from the oxygenation sensor, indicating an additionaltissue oxygenation percentage associated with an additional exercise;

receive an indication of a distance associated with the additionalexercise; and

calculate an expected time to completion of the additional exercise,based on the calculated critical tissue oxygenation.

-   5. The apparatus of any of the preceding Paras, wherein the    oxygenation sensor utilizes near infra-red spectroscopy.-   6. The apparatus of any of the preceding Paras, wherein the body    tissue is a muscle.-   7. The apparatus of Para 6, wherein the muscle is classified as an    inactive muscle for the type of exercise undertaken by the user for    the at least two exercise periods.-   8. The apparatus of any of the preceding Paras, wherein the    apparatus is configured to be worn on an appendage of a user.-   9. The apparatus of any of the preceding Paras, wherein the    regression is a linear regression.-   10. The apparatus of any of the preceding Paras, wherein the    regression is a curvilinear regression.-   11. An apparatus, comprising:

a processor;

an oxygenation sensor, configured to be positioned proximate an area ofskin of a user, the oxygenation sensor further configured to output dataindicative of a tissue oxygenation of a body tissue of the user; and

a non-transitory computer-readable medium comprising computer-executableinstructions that when executed by the processor are configured toperform at least:

-   -   receive periodic data from the oxygenation sensor indicative of        a tissue oxygenation percentage of the body tissue of the user;    -   calculate a tissue oxygenation trend using two or more received        tissue oxygenation data points,    -   wherein:    -   if the tissue oxygenation trend is negative, output a signal        indicating that the user is exercising at an unsustainable work        rate,    -   if the tissue oxygenation trend is positive, output a signal        indicating that the user is exercising at a sustainable work        rate, and/or    -   if the tissue oxygenation trend is level, output a signal        indicating that the user is exercising at a critical work rate.

-   12. The apparatus of Para 11, wherein if a negative tissue    oxygenation trend has an absolute value above a threshold value,    output a signal indicating that the user is exercising at a severe    intensity above a critical power for the user.

-   13. The apparatus of Para 11 or 12, wherein if the tissue    oxygenation trend is level, output a critical tissue oxygenation    percentage equal to a tissue oxygenation percentage corresponding to    the level tissue oxygenation trend.

-   14. The apparatus of any of Paras 11 to 13, wherein the tissue    oxygenation trend is calculated as a change in tissue oxygenation    percentage equal to a difference between a current rolling average    tissue oxygenation and a previous rolling average tissue    oxygenation.

-   15. The apparatus of any of Paras 11 to 14, wherein the tissue    oxygenation trend is level when a change in tissue oxygenation    percentage is less than a threshold change value for at least a    threshold time.

-   16. The apparatus of Para 15, wherein the threshold change value is    0.1% and the threshold time is 3 seconds.

-   17. The apparatus of any of Paras 11 to 16, wherein the body tissue    is a muscle.

-   18. The apparatus of any of Paras 11 to 17, wherein the oxygenation    sensor utilizes near infra-red spectroscopy.

-   19. The apparatus of any of Paras 11 to 18, wherein the apparatus is    configured to be worn on an appendage of a user.

-   20. A method, comprising:

receiving, by a processor, sensor data from an oxygenation sensorindicative of a tissue oxygenation percentage of a body tissue of auser;

calculating, by the processor, a tissue oxygenation trend using two ormore received tissue oxygenation data points,

wherein:

if the tissue oxygenation trend is negative, outputting a signalindicating that the user is exercising at an unsustainable work rate,

if the tissue oxygenation trend is positive, outputting a signalindicating that the user is exercising at a sustainable work rate,and/or

if the tissue oxygenation trend is level, outputting a signal indicatingthat the user is exercising at a critical work rate.

-   21. The method of Para 20, wherein the tissue oxygenation trend is    calculated as a change in tissue oxygenation percentage equal to a    difference between a current rolling average tissue oxygenation and    a previous rolling average tissue oxygenation.-   22. The method of Para 20 or 21, wherein if a negative tissue    oxygenation trend has an absolute value above a threshold value,    output a signal indicating that the user is exercising at a severe    intensity above a critical power for the user.

The present application also extends to the subject-matter described inthe following numbered paragraphs (referred to as “Para” or “Paras”):

-   1. An apparatus, comprising:

a processor;

an interface;

an oxygenation sensor, configured to be positioned proximate an area ofskin of a user, the oxygenation sensor further configured to output dataindicative of a tissue oxygenation of a body tissue of the user;

a power sensor configured to output data indicative of a powerconsumption of the user; and

a non-transitory computer-readable medium comprising computer-executableinstructions that when executed by the processor are configured toperform at least:

-   -   receive tissue oxygenation data from the oxygenation sensor        during an exercise session comprising at least a portion of a        total exercise time exercising within a severe exercise        intensity domain;    -   calculate a change in tissue oxygenation as a difference between        a current tissue oxygenation value and a previous tissue        oxygenation value; and    -   compare the change in tissue oxygenation to a threshold change        value and a threshold duration,    -   wherein:    -   if the change in tissue oxygenation is less than or equal to the        threshold change value, and a change in tissue oxygenation        persists for a first duration greater than or equal to the        threshold duration, output a signal to the interface indicating        a critical power value equal to a current power indicated by the        power sensor, and/or    -   if the change in tissue oxygenation is greater than the        threshold change value or the change in tissue oxygenation        persists for a second duration less than the threshold duration,        output a signal to the interface indicating that the current        power indicated by the power sensor is not equal to the critical        power of the user.

-   2. The apparatus of Para 1, wherein the current tissue oxygenation    value and the previous tissue oxygenation value are rolling averages    of tissue oxygenation data points received from the oxygenation    sensor during a rolling average duration.

-   3. The apparatus of Para 2, wherein the rolling average duration is    at least two seconds.

-   4. The apparatus of any of the preceding Paras, wherein the body    tissue is a muscle.

-   5. The apparatus of any of the preceding Paras, wherein the    oxygenation sensor utilizes near infra-red spectroscopy.

-   6. The apparatus of any of the preceding Paras, wherein the    apparatus is configured to be worn on an appendage of a user.

-   7. The apparatus of any of the preceding Paras, wherein the    computer-readable instructions, when executed by the processor,    further cause the apparatus to:

calculate, an anaerobic work capacity for the user equal to a summationof a plurality of positive difference values for the total exercisetime, wherein a difference value is equal to a difference between anoutput power value from the power sensor and the critical power value.

-   8. An apparatus, comprising:

a processor;

an interface;

an oxygenation sensor, configured to be positioned proximate an area ofskin of a user, the oxygenation sensor further configured to output dataindicative of a tissue oxygenation of a body tissue of the user;

a power sensor configured to output data indicative of a powerconsumption of the user; and

a non-transitory computer-readable medium comprising computer-executableinstructions that when executed by the processor are configured toperform at least:

-   -   receive tissue oxygenation data from the oxygenation sensor        during an exercise session comprising at least a portion of a        total exercise time exercising within a severe exercise        intensity domain;    -   calculate a change in tissue oxygenation as a difference between        a current tissue oxygenation value and a previous tissue        oxygenation value; and    -   compare the change in tissue oxygenation to a threshold change        value,    -   wherein:    -   if the change in tissue oxygenation is less than or equal to the        threshold change value, output a signal to the interface        indicating a critical power value equal to a current power        indicated by the power sensor, and/or    -   if the change in tissue oxygenation is greater than the        threshold change value, output a signal to the interface        indicating that the current power indicated by the power sensor        is not equal to the critical power of the user.

-   9. The apparatus of Para 8, wherein the power sensor comprises an    accelerometer.

-   10. The apparatus of Para 8 or 9, wherein the power sensor comprises    a dynamometer.

-   11. The apparatus of any of Paras 8 to 10, wherein the interface    comprises a graphical user interface.

-   12. The apparatus of any of Paras 8 to 11, wherein the interface    comprises a transceiver.

-   13. The apparatus of any of Paras 8 to 12, wherein the    computer-readable instructions, when executed by the processor,    further cause the apparatus to:

calculate, an anaerobic work capacity for the user equal to a summationof a plurality of positive difference values for the total exercisetime, wherein a difference value is equal to a difference between anoutput power value from the power sensor and the critical power value.

-   14. The apparatus of any of Paras 8 to 13, wherein the body tissue    is an active muscle for the exercise session.-   15. The apparatus of any of Paras 8 to 13, wherein the body tissue    is an inactive muscle for the exercise session.-   16. A non-transitory computer-readable medium comprising    computer-executable instructions that when executed by a processor    are configured to perform at least:

receive, from an oxygenation sensor, tissue oxygenation data during anexercise session comprising at least a portion of a total exercise timeexercising within a severe exercise intensity domain;

calculate a change in tissue oxygenation as a difference between acurrent tissue oxygenation value and a previous tissue oxygenationvalue; and

compare the change in tissue oxygenation to a threshold change value,

wherein:

if the change in tissue oxygenation is less than or equal to thethreshold change value, output a signal to an interface indicating acritical power value equal to a current power indicated by the powersensor, and

if the change in tissue oxygenation is greater than the threshold changevalue, output a signal to the interface indicating that the currentpower indicated by the power sensor is not equal to the critical powerof the user.

-   17. The non-transitory computer-readable medium of Para 16, wherein    the current tissue oxygenation value and the previous tissue    oxygenation value are rolling averages of tissue oxygenation data    points received from the oxygenation sensor during a rolling average    duration.-   18. The non-transitory computer-readable medium of Para 17, wherein    the rolling average duration is at least two seconds.-   19. The non-transitory computer-readable medium of any of Paras 16    to 18, wherein the oxygenation sensor utilizes near infra-red    spectroscopy.-   20. The non-transitory computer-readable medium of any of Paras 16    to 19, wherein the rolling average duration is ranges between least    1 and 10 seconds.

The present application also extends to the subject-matter described inthe following numbered paragraphs (referred to as “Para” or “Paras”):

-   1. An apparatus, comprising:

a processor;

a sensor; and

a non-transitory computer-readable medium comprising computer-executableinstructions that when executed by the processor are configured toperform at least:

-   -   receive a plurality of sensor data points from the sensor, the        plurality of sensor data points each indicating an instantaneous        speed of a user during an exercise session, the exercise session        having a prescribed duration between a start time and an end        time;    -   calculate an end speed of the user at the end of the prescribed        duration of the exercise session;    -   calculate a distance above end speed as a total distance        travelled by the user at an instantaneous speed above the        calculated end speed between the start time and end time of the        prescribed duration of the exercise session;    -   output a critical speed of the user based on the calculated end        speed, and/or output an anaerobic work capacity based on the        calculated distance above end speed.

-   2. The apparatus of Para 1, wherein the end speed of the user is    calculated as an average of a sub-set of the plurality of periodic    sensor data points received during an end portion of the exercise    session.

-   3. The apparatus of Para 2, wherein the end portion of the    prescribed duration of the exercise session comprises a last 30    seconds of the prescribed duration.

-   4. The apparatus of any of the preceding Paras, wherein the critical    speed is calculated, by the processor, as 90-95% of the calculated    end speed.

-   5. The apparatus of any of the preceding Paras, wherein the    anaerobic work capacity is calculated as 125-135% of the calculated    distance above end speed.

-   6. The apparatus of any of the preceding Paras, wherein the    plurality of sensor data points are periodic.

-   7. The apparatus of any of the preceding Paras, wherein the exercise    session comprises a prescribed duration of 2-5 minutes.

-   8. The apparatus of any of the preceding Paras, wherein the exercise    session comprises a prescribed duration of approximately 3 minutes.

-   9. The apparatus of any of the preceding Paras, wherein the exercise    session further prescribes that the user exercises at a highest    subjective intensity level for the prescribed duration.

-   10. The apparatus of any of the preceding Paras, wherein the sensor    comprises an accelerometer.

-   11. The apparatus of any of the preceding Paras, wherein the sensor    comprises a location-determining sensor.

-   12. An apparatus, comprising:

a processor;

a sensor; and

a non-transitory computer-readable medium comprising computer-executableinstructions that when executed by the processor are configured toperform at least:

-   -   receive data from the sensor associated with at least two        exercise periods, at least a portion of the at least two        exercise periods associated with a severe exercise intensity        domain, and having differing durations;    -   calculate an exercise period summary data point for each of the        at least two exercise periods as a total distance travelled        versus a duration of exercise;    -   calculate a regression through the exercise period summary data        points for the at least two exercise periods;    -   output a critical speed for the user equal to a slope of at        least a portion of the regression, and/or output an anaerobic        work capacity equal to an intercept associated with at least a        portion of the regression.

-   13. The apparatus of Para 12, wherein the computer-readable    instructions, when executed by the processor, further cause the    apparatus to:

receive data, from the sensor, indicating a speed associated with anadditional exercise period; and

compare the speed to the critical speed,

wherein:

if the speed is greater than the critical speed output a signalindicating that the user is exercising at an unsustainable work rate,and/or

if the speed is less than or equal to the critical speed, output asignal indicating that the user is exercising at a sustainable workrate.

-   14. The apparatus of Para 12 to 13, wherein the sensor is a    location-determining sensor.-   15. The apparatus of any of Paras 12 to 14, wherein the sensor is an    accelerometer.-   16. The apparatus of any of Paras 12 to 15, wherein the apparatus is    configured to be worn on an appendage of a user.-   17. The apparatus of any of Paras 12 to 16, wherein the regression    is a linear regression.-   18. The apparatus of any of Paras 12 to 17, wherein the regression    is a curvilinear regression.-   19. A method, comprising:

receiving, by a processor, a plurality of sensor data points from asensor, the plurality of sensor data points indicating a speed of a userat a plurality of time periods during an exercise session, the exercisesession having a prescribed duration between a start time and an endtime;

calculating, by the processor, an end speed of the user at the end ofthe exercise session;

calculating, by the processor, a distance above end speed as a totaldistance travelled by the user at a speed above the calculated end speedbetween the start time and end time of the prescribed duration of theexercise session;

output a critical speed of the user based on the calculated end speed,and/or output an anaerobic work capacity based on the calculateddistance above end speed.

-   20. The method of Para 19, wherein the end speed of the user is    calculated as an average of a sub-set of the plurality of periodic    sensor data points received during an end portion of the exercise    session.-   21. The method of Para 20, wherein the end portion of the prescribed    duration of the exercise session comprises a last 30 seconds of the    prescribed duration.-   22. The method of any of Paras 19 to 21, wherein the critical speed    is calculated, by the processor, as 90-95% of the calculated end    speed.-   23. The method of any of Paras 19 to 22, wherein the anaerobic work    capacity is calculated as 125-135% of the calculated distance above    end speed.-   24. The method of any of Paras 19 to 23, wherein the plurality of    sensor data points are periodic.-   25. The method of any of Paras 19 to 24, wherein the exercise    comprises a prescribed duration of 2-5 minutes.-   26. The method of any of Paras 19 to 25, wherein the exercise    session comprises a prescribed duration of approximately 3 minutes.-   27. The method of any of Paras 19 to 26, wherein the exercise    session further prescribes that the user exercises at a highest    subjective intensity level for the prescribed duration.

The present application also extends to the subject-matter described inthe following numbered paragraphs (referred to as “Para” or “Paras”):

-   1. A method, comprising:

receiving a data point from a user indicative of an athletic performancedistance, d, and athletic performance time, t;

calculating a critical velocity fraction, CV_(fraction), based on theathletic performance time and activity type;

calculating an average velocity for the user as the athletic performancedistance divided by the time; and

calculating a critical velocity for the user equal to the averagevelocity divided by the critical velocity fraction,

wherein CV_(fraction) is approximately equal to 2*f^(−0.1) for theactivity type corresponding to running.

-   2. The method of Para 1, wherein the CVfraction is approximately    equal to 1.9*t-0.08.-   3. The method of Para 1, wherein the CVfraction is approximately    equal to 1.87*t-0.082.-   4. The method of Para 1, wherein the CVfraction is approximately    equal to 1.868*t-0.082.-   5. The method of Para 1, wherein the CVfraction is approximately    equal to 1.8677*t-0.082.-   6. The method of any of the preceding Paras, further comprising:

calculating a distance travelled below the critical velocity as theaverage velocity multiplied by the athletic performance time; and

calculating an anaerobic work capacity equal to the athletic performancedistance minus the distance travelled below the critical velocity.

-   7. A method, comprising:

receiving a data point from a user indicative of an athletic performancedistance, d, and athletic performance time, t;

calculating a critical velocity fraction, CV_(fraction), based on theathletic performance distance and activity type;

calculating an average velocity for the user as the athletic performancedistance divided by the time; and

calculating a critical velocity for the user equal to the averagevelocity divided by the critical velocity fraction,

wherein CV_(fraction)=2*d^(−0.1) for the activity type corresponding torunning.

-   8. The method of Para 7, wherein the CV_(fraction) is approximately    equal to 2.2*d^(−0.09).-   9. The method of Para 7, wherein the CV_(fraction) is approximately    equal to 2.24*d^(−0.09).-   10. The method of Para 7, wherein the CV_(fraction) is approximately    equal to 2.240*d^(−0.09)-   11. The method of Para 7, wherein the CV_(fraction) is approximately    equal to 2.2398*d^(−0.09)-   12. A method, comprising:

receiving a data point from a user indicative of an athletic performancepower, p, and athletic performance time, t;

calculating a critical power fraction, CP_(fraction), based on theathletic performance time and activity type;

calculating an average power for the user as the athletic performancepower divided by the time; and

calculating a critical power for the user equal to the average powerdivided by the critical power fraction,

wherein CP_(fraction)=2*t^(−0.1) for the activity type corresponding tocycling.

-   13. The method of Para 12, wherein the CP_(fraction) is    approximately equal to 1.9*t^(−0.08)-   14. The method of Para 12, wherein the CP_(fraction) is    approximately equal to 1.92*t^(−0.088).-   15. The method of Para 12, wherein the CP_(fraction) is    approximately equal to 1.920*t^(−0.088).-   16. The method of Para 12, wherein the CP_(fraction) is    approximately equal to 1.9199*t^(−0.088).-   17. A method, comprising:

receiving a data point from a user indicative of an athletic performancepower, p, and athletic performance time, t;

calculating a critical power fraction, CP_(fraction), based on theathletic performance power and activity type;

calculating an average power for the user as the athletic performancepower divided by the time; and

calculating a critical power for the user equal to the average powerdivided by the critical power fraction,

wherein CP_(fraction)=3*p^(−0.1) for the activity type corresponding tocycling.

-   18. The method of Para 17, wherein the CP_(fraction) is    approximately equal to 3.1*p^(−0.08).-   19. The method of Para 17, wherein the CP_(fraction) is    approximately equal to 3.09*p^(−0.086).-   20. The method of Para 17, wherein the CP_(fraction) is    approximately equal to 3.089*p^(−0.086).-   21. The method of Para 17, wherein the CP_(fraction) is    approximately equal to 3.0889*p^(−0.086).

The present application also extends to the subject-matter described inthe following numbered paragraphs (referred to as “Para” or “Paras”):

-   1. A method, comprising:

receiving questionnaire responses from a user;

calculating and storing a user athletic profile based on thequestionnaire responses;

receiving a rate of perceived exertion value from the user following anexercise session;

mapping the rate of perceived exertion value to an oxygenationconsumption scale, based upon the stored athletic profile; and

outputting an estimated volume of oxygen consumption of the user, basedupon the mapping.

-   2. A method of Para 1, wherein the questionnaire asks a user to    estimate one or more attributes selected from questions consisting    of: an estimation of bone size, an estimation of leanness of the    user, an estimation of muscle size, an estimation of sleep quality,    an estimation of relaxation habits, an estimation of nutrition    quality, an estimation of smoking status, an estimation of drinking    habits, and an estimation of an activeness of the user, the user's    age, gender, height, waist circumference, weight, an indication as    to whether the user is pregnant, an estimation of a 5 km running    race pace, and an estimation of a number of days active during the    week.

To avoid unnecessary duplication of effort and repetition of text,certain features are described in relation to only one or severalaspects, embodiments or Paragraphs. However, it is to be understoodthat, where it is technically possible, features described in relationto any aspect, embodiment or Paragraph may also be used with any otheraspect, embodiment or Paragraph.

We claim:
 1. A method, comprising: receiving questionnaire responsesfrom a user; calculating and storing a user athletic profile based onthe questionnaire responses; receiving a rate of perceived exertionvalue from the user following an exercise session; mapping the rate ofperceived exertion value to an oxygenation consumption scale, based uponthe stored athletic profile; and outputting an estimated volume ofoxygen consumption of the user, based upon the mapping.
 2. A method ofclaim 1, wherein the questionnaire asks a user to estimate one or moreattributes selected from questions consisting of: an estimation of bonesize, an estimation of leanness of the user, an estimation of musclesize, an estimation of sleep quality, an estimation of relaxationhabits, an estimation of nutrition quality, an estimation of smokingstatus, an estimation of drinking habits, and an estimation of anactiveness of the user, the user's age, gender, height, waistcircumference, weight, an indication as to whether the user is pregnant,an estimation of a 5 km running race pace, and an estimation of a numberof days active during the week.